All authors contributed to this study, including the methodology, experiments, and analysis. All authors read and approved the final manuscript.

In recent years, an increasing number of studies have combined FL with machine learning, deep learning, and data mining to protect images10. In order to achieve private transmission of facial images between different users, Yang et al. proposed a facial-image privacy protection method based on FL and ensemble models25. The clients obtain a local face recognition model through local training. And the server integrates the clients’ face recognition model. Experiments show that this method generates high-quality, transferable, and private face images more efficiently than other existent methods. To address confidentiality and privacy issues in shared medical data, the work26 uses the public repository the cancer genome atlas (TCGA) dataset to simulate a distributed environment and applies a differentially private FL framework to analyze histopathology images.

As a distributed machine learning framework, federated learning (FL) enables multiple clients to collaboratively train models while adhering to stringent privacy protection, data security, and regulatory requirements9. This framework is extensively utilized in fields such as healthcare, the internet of things, and intelligent transportation. Specifically, FL builds a global model on a central server and starts the local training process on the client side. The client sends updated local parameters to the central server for aggregation after training. Finally, a global model with generalization ability is obtained. In this process, there is no need to upload local data to the server, thus ensuring privacy security and avoiding the risk of sensitive data being attacked10. Additionally, the efficiency of data utilization is greatly improved by client cooperation.

Hou, S. et al. Text-aware single image specular highlight removal. In Pattern Recognition and Computer Vision: 4th Chinese Conference, PRCV 2021, Beijing, China, October 29–November 1, 2021, Proceedings, Part IV 4 115–127 (Springer, 2021).

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• Importance - Modo uses an adaptive light sampling method to evaluate lighting in a scene. This option allows you to control illumination sampling using a single global value, producing better results in less time. In rare cases, where adaptive sampling isn't producing the desired outcome, an individual light's Importance value can be adjusted, acting as a multiplier to the lights evaluated importance. Changes in Importance are relative to other lights in the scene, influencing it one way or the other as more or less important, in effect increasing or decreasing its sample allocation from the total number of possible Light Samples, all while remaining fully unbiased.

Note:  It should be noted that by default, new items do not have any transform items associated with them (even though they are visible here within the Properties panel). This is useful as an optimization, as only the necessary transforms are created on an as-needed basis, reducing scene overhead. There are several actions that add these base transform items. One is by simply transforming the target item with one of the various transform tools or by editing the values input fields. This action causes the particular transform item to be added automatically to the Channels viewport list. Due to this fact, you may need to specifically create item transforms when Referencing, because in order to override the channels in the master scene, they must first exist.

In addition to the intuitive comparison of the effects of highlight removal using five different methods, we also compare the text recognition performance of images processed by these methods. As depicted in Fig. 4, it is clearly observed that images with highlights that are not processed exhibit low text recognition performance, with the parts obscured by highlights failing to be normally recognized, detecting only 14 pieces of text information. Images processed with other methods achieve limited highlight removal, detecting 15 pieces of text data. Meanwhile, FL-AttGAN, utilizing its attention mechanism to focus on the highlight areas, successfully detects 16 pieces of text data completely.

Supplies a large range of Machine Vision Lighting, LED Ring, coaxial, Bar, Line, UV and IR Collimated lighting, Strobe and continuous lighting controller.

• Item Group - determines the specific group of item layers in the scene that is affected by the Light Linking. The group needs to be defined in the Groups viewport panel. This can be done easily by selecting the target items while in Items mode and then, in the Groups palette, click the New Group button. Define a name for the group in the pop-up dialog and choose the From Selected Items option and click OK to accept. Once the group is defined, select the named group here.

In this paper, we have developed the FL-AttGAN method for highlight removal, providing a novel approach for multi-user text highlight removal under the premise of big data security. Unlike existing methods,we employ an attention mechanism that can more precisely focus on highlight areas and their surrounding detail features, thereby enhancing the model’s representational capability. This approach effectively removes highlights while preserving the details of the image. Furthermore, we employ federated learning to reduce the risk of data breaches and ensure that sensitive information remains on the client’s device. Evaluations conducted on the SD1, SD2 and RD datasets demonstrate that our method achieves optimal results in image highlight removal.

In feature representation calculation, the relative importance of each feature determines its assigned weight. Features deemed more important receive higher weights, significantly influencing the final feature representation. Since these weights are computed based on a comprehensive analysis of the entire input feature set, applying the weight matrix \(\textbf{A}\) to the feature matrix \(\textbf{VX}\) results in an integrated feature representation. This representation not only preserves information from individual features but also incorporates insights from other features, capturing broader contextual dependencies on a more macro level.

• Dissolve - when the Dissolve function is set to any value above 0%, the light's overall effect on the scene attenuate as the value increases. When set to 100%, the light's effect on the scene is completely disabled. This function provides a convenient way to dim a light's effect within a scene.

Table 3 presents the performance results of the highlight removal network with federated learning, compared to the highlight removal network without federated learning as shown in Table 2. The performance metrics indicate a slight decrease with federated learning. However, the results in Table 2 assume that all data resides on a single client, whereas, in practice, the data is distributed across multiple clients. By employing federated learning algorithms, we can leverage data from multiple clients to train a global model. The performance in this federated learning scenario is comparable to that in the ideal scenario where data is assumed to be uniformly distributed on a single client.

Another type of LED uses a quantum dot which can have its properties and wavelength adjusted by its size. Light-emitting diodes are widely used in indicator and ...

We conduct an evaluation of the proposed FL-AttGAN by comparing it to four other methods using the same federated framework on the SD1, SD2, and RD datasets. Among these methods, the first is the federated GAN method 31 (denoted as “FL-GAN”). The second method is the federated CycleGAN method 29 (denoted as “FL-CycleGAN”). The third method is the federated UNet method 28 (denoted as “FL-UNet”). The fourth method combines federated learning with HQG-Net 30 (denoted as “FL-HQG-Net”). Additionally, for comparison, the SSIM, PSNR, Recall, and Precision of highlight images (denoted as “Light”) are also tested on the same datasets, serving as benchmarks.

The Spot Light looks and acts like a real world spot light and obeys the physical law of inverse square falloff of intensity. Light radiates from a single point outward in a cone shape, widening the further the subject is from the light itself. As with all items, the spot light controls are located on the Properties tab when the light is selected. Spot lights have Position and Rotation options, as well as controls for the intensity of the light, shadow type, and other settings covered fully below. Using the Transform tool with the Spot Light selected provides interactive handles for editing position, rotation, cone, and soft edge angles.

The client trains the local image pairs to achieve the specular detection from visual data , which (i) efficiently avoids overestimation and improves representational ability; (ii) provides the improved detection accuracy compared to existent methods.

In our local model, AttGAN, we employ attention mechanisms and adversarial strategies to enhance the precision of highlight removal. Specifically, the attention mechanism focuses more precisely on highlighted areas, effectively removes highlights while preserving the original image details; the adversarial strategy centers on refining the generator’s output through a consistent feedback loop from the discriminator, which allows the generator to learn from its mistakes and progressively enhance its ability to produce visually convincing and realistic images, thereby achieving high-performance highlight removal.

First, we assess the performance of FL-AttGAN in the task of highlight removal. In the absence of federated learning, we compare the proposed local highlight removal network AttGAN with several variants: AttGenetor, which removes the discriminator network (\({\text{G}}\)); a GAN network without the attention mechanism; and a generator network that excludes both the discriminator and the attention mechanism. The results of the highlight removal performance are shown in Table 1. The results indicate that each module of AttGAN is indispensable. The generator without the attention mechanism and discriminator exhibits the lowest performance metrics in terms of Precision, Recall, PSNR, and SSIM. The inclusion of the discriminator improves these four evaluation metrics, while the addition of the attention mechanism (in the variant AttGenetor) plays an even more significant role. When both the discriminator and the attention mechanism are employed in the AttGAN approach, the best overall evaluation metrics are achieved. This confirms that the attention mechanism effectively captures important features, and the discriminator significantly enhances the quality of the generated images.

where \(\alpha\), \(\beta\) and \(\gamma\) are the learning rates of highlight detection network, highlight removal network and discriminator, respectively.

• Shadow Map Res - sets the resolution for the calculated deep shadow map in pixels. This option is only available when Deep Shadow Maps is selected.

where \(\mu _f\) and \(\mu _k\) are the f-th layer feature map of CTPN and the k-th layer feature map of the DenseNet, respectively.

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• Mode - determines whether the light is Included, meaning it only affects the items in the specified item group, and Excluded by all other surfaces, meaning it is ignored by any items in the specified item group.

Furthermore, we detect text quality by calculating the metrics recall and precision, and utilize PSNR and SSIM to evaluate visual quality.

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• Shadow Type - offers options between Ray Traced, None and Deep Shadow Maps. In situations where you want a light to cast a shadow, Ray Traced gives the most accurate results. The traditional hard edge of ray traced shadows can easily be softened using Spread Angle value in any light item. Deep Shadow Maps are useful for volumetric lights and fur rendering, where a great deal of calculations are required to produce shadows; producing similar results to ray traced shadows while reducing the number of calculations.

Each element within \(\hat{{\textbf{M}}}^{n}\) has a value between 0 and 1, with higher values indicating a greater likelihood that the corresponding region of image \({{\textbf{S}}}^{n}\) is influenced by specular highlights. Since \({\textbf{{S}}}{^{n}}\) and \(\hat{\textbf{{M}}}{^{n}}\) have identical dimensions, the highlight detection network adopts a fully convolutional structure (FCN), featuring three downsampling layers and three upsampling layers. Each upsampling layer is followed by three convolutional layers, while each downsampling layer is followed by two convolutional layers.

The numerical evaluation is conducted via a TensorFlow framework, on a desktop equipped with a 12 vCPU Intel(R) Xeon(R) Platinum 8255C CPU @2.50GHz, 40GB RAM, and Nvidia GPU RTX 2080 Ti.

Tip:  Keep in mind that a light's intensities and falloffs adhere to real world scale values, scenes that do not adhere to a real world scale factor likely require intensities to be adjusted accordingly to achieve appropriate illumination.

He, C. et al. Hqg-net: Unpaired medical image enhancement with high-quality guidance. IEEE Trans. Neural Netw. Learn. Syst. (2023).

Inspired by these efforts, we offer a novel solution to the challenge of removing highlights from images by integrating attention mechanisms into GAN. Our attention-directed GAN not only reduces the presence of highlights, but also ensures that the reconstructed image retains its original texture and detail, enabling a more effective highlight removal process.

Klinker, G. J., Shafer, S. A. & Kanade, T. Using a color reflection model to separate highlights from object color. In Image Understanding Workshop: Proceedings of a Workshop Held at Los Angeles, California, February 23-25, 1987, vol. 2, 614 (Morgan Kaufmann Publishers, 1987).

• Radiant Intensity - controls the intensity of the light and uses the standard physically-based unit of Watts per volumetric meter. As you would expect, increasing this value increases the amount of light coming from the Spot Light and decreasing the value reduces the light intensity. With spot lights, the area of light generated in the scene is very small and therefore large values are likely necessary to illuminate the scene how you would expect.

Kong, W. et al. A practical solution for non-intrusive type ii load monitoring based on deep learning and post-processing. IEEE Trans. Smart Grid 11, 148–160 (2019).

In this work, we focus on recovering text that is occluded by highlights. To this end, we apply pre-trained text detection and recognition models to supervise text recovery.

• Falloff Type - light in the real world isn't a uniform brightness; its intensity diminishes with distance. Photographers may be familiar with the concept that a light is a quarter as bright at twice the distance away, known as the Inverse Square Law. Modo lights default to this setting, providing a realistic way to light a scene. However, there are times when you may not wish to have this behavior, so three falloff type options are provided:

For the global model, we apply the FL framework. Firstly, it defines the global removal network at a central server. Each client then trains the local model and transmits the updated model parameters to the central server for aggregation, rather than the raw data itself. The use of federated learning greatly reduces the risk of data breaches and ensures that sensitive information remains on the client’s device.

Li, S. et al. Whu-stereo: A challenging benchmark for stereo matching of high-resolution satellite images. IEEE Trans. Geosci. Remote Sens. 61, 1–14 (2023).

• Add - adds additional Transform items to the associated item or, if they do not yet exist, it simply adds them. Transform items are the channel groups that store the actual transform values, controlling any item's position, rotation, and/or scale. You can add as many transform items as you want for any transform property desired. Adding additional Transform items produces an additive effect where each transform group is evaluated before the next, and so on. Additional item transforms are evaluated in their order in the Channels list, from the bottom upwards.

He, C. et al. Camouflaged object detection with feature decomposition and edge reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 22046–22055 (2023).

• Soft Edge - defines an illumination falloff inside the edge of the spot lights cone producing a more natural look. The soft edge is contained within the Cone Angle, and attenuates illumination at the edges. A 40° Cone Angle with a 10° falloff would really be a 20° cone angle of full illumination that would falloff 10° across the outer edges of the cone. This value can also be set interactively with the Transform tool in the 3D viewport.

Su, P., Zhao, H. & Wang, Y. A novel model based on big data environment for text content security recognition. J. Signal Process. Syst. 1, 1–14 (2024).

• Control Light Linking - illumination on a surface is generally controlled by the Shader item in the Shader Tree. Within the shader, it's possible to control a light's effect on a surface with Light Linking. As its name describes, it links the illumination affects of a group of lights to specific items or material groups. When the Control Light Linking option is enabled on a light item, it acts as an individual light-specific override to the shader, allowing you to Include or Exclude a specific light's illumination on a group of items.

In Fig. 3, we visually compare the highlight removal effectiveness of our FL-AttGAN against four competitors, using several images as examples. We can observe that for images with highlights, these often obscure and impact text legibility, making accurate identification challenging even for the human eye. Although images processed by other methods show some restoration of text clarity and successful highlight removal, each image exhibits partial shadows, which may interfere with subsequent text recognition by the Paddle OCR network. In contrast, images processed by FL-AttGAN not only effectively remove highlights but also avoid introducing new shadows that could impede text recognition. This advantage is due to the attention mechanism within FL-AttGAN, which can effectively identify and focus on areas of high brightness in the image. Training with generative adversarial networks enhances the capability to detect and remove highlighted regions, thus improving the overall image clarity for optical character recognition tasks.

For each client, we integrate an attention mechanism into the GAN-based framework, enabling the model to focus on highlighted areas and their surrounding detailed features, thereby enhancing the model’s representational capability. This effectively removes highlights while preserving image details.

(2) highlight removal network The highlight removal network \({\text{R}}\) is employed to eliminate the specular highlight and restore the textual content. Therefore, we take the generated mask \(\hat{\textbf{{M}}}{^{n}}\) as input to generate the corresponding highlight-free image \(\hat{\textbf{I}}^{n}\):

With the rapid development of neural networks, domestic and foreign scholars use neural networks to detect highlight areas of images containing different information. The work17 trains the residual convolutional neural network (CNN) and uses weakly labeled data to locate and remove specular highlights in endoscopic images. In recent years, GAN have been well developed and used in many fields. For example, drawing inspiration from the prey–predator dynamic, He et al. develop an adversarial training framework to address the issue of limited accuracy in camouflaged object detection 18. This framework focuses on the dynamic confrontation between the generator and the detector. As the generator continuously introduces new challenges, the detector should enhance its performance to meet the challenges posed by the generator. Zhu et al. propose a method to remove highlights in facial images19. Following the structure of conditional GAN, they take face images with specular highlights as conditions and predict the corresponding images without highlights. At the same time, a novel mask loss is introduced through highlight detection, aiming to make the network focus more on highlight areas. These methods usually remove specular highlights from medical images and specific object images, but cannot handle images with text. To solve this challenging problem, the work13 utilizes dual-network structure, namely a highlight detection network and a highlight removal network to remove images with text information.

Suo, J. et al. Fast and high quality highlight removal from a single image. IEEE Trans. Image Process. 25, 5441–5454 (2016).

SpotLights

By using these loss functions, the parameters of the networks \(\textbf{w}_h^c\), \(\textbf{w}_r^c\) and \(\textbf{w}_g^c\) are updated by stochastic gradient descents (SGD).

When light shines on a particular matter suspended in the air, such as water vapor, smoke, or even pollution, the light rays become visible. In computer graphics, this effect is known as "volumetric lights". Their affect can be subtle or pronounced and, either way, it can add a good amount of believability and atmosphere to renders. Additional settings related to volumetrics can be found in the Light Material item.

We numerically evaluate our FL-AttGAN method and its four competitors using SD113, SD213 and RD13 datasets. The results show that FL-AttGAN not only displays superior highlight removal visually without introducing shadow interference like its four competitors, but it also excels in quantitative analyses across multiple metrics, including PSNR, SSIM, recall, and precision.

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Zheng, Y., Gao, Y. Specular highlight removal by federated generative adversarial network with attention mechanism. Sci Rep 14, 23472 (2024). https://doi.org/10.1038/s41598-024-74229-3

• Samples - when the Direct Illumination option is set above zero, the Samples value is grayed out as there is no need for you to adjust it. The number of samples are dynamically allocated using the adaptive light sampling method. This provides you a global control to adjust overall shading and shadow quality. When the Direct Illumination option is set to '0', this disables adaptive sampling and you can manually adjust this Samples value to control light and shadow quality per light.

• Cone Angle - spot lights emit their light in a cone shape that originates from the light's position toward the default Z direction. The Cone Angle defines the shape of the cone that is generated; larger values produce wider conical shapes and therefore larger areas of illumination, while smaller values produce tighter cones with smaller areas of illumination. A setting of 360° produces light similar to a point light radiating in all directions equally. This value can also be set interactively with the Transform tool in the 3D viewport.

To further quantify the performance of generation, we employ PSNR, SSIM, Recall, and Precision to evaluate the highlight removal and text detection performance for different methods in the SD1, SD2 and RD datasets, with the results presented in Table 3. By observation, consistent with the results of visual analysis, our FL-AttGAN approaches demonstrate advantages over other methods across the four evaluation metrics on the three datasets, particularly in FSNR and Recall rates, which are the metrics most affected by highlights. In terms of precision, the slight advantage is due to the inherently high accuracy of paddleOCR; once we successfully detect and recall the text information, high-performance text recognition is achieved. Furthermore, FL-UNet demonstrates the lowest performance, attributed to its structure having only a single generator without additional mechanisms for performance enhancement.

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Subsequently, the attention weights matrices \(\textbf{A}\) are calculated by softmax function to get the attention distribution of the feature map \(\textbf{z}_l^{\textsf{T}}\).

The RD dataset is a real-world dataset comprising 2025 image pairs: each pair consists of an image with text-aware specular highlights, the corresponding highlight-free image, and a binary mask image indicating the location of the highlights. The image content includes ID cards and driver’s licenses, which contain substantial textual information. In the data collection process, a transparent plastic film was placed on the images, and lighting was turned on. The camera then captured the images to obtain those with highlights. Correspondingly, highlight-free images were obtained by turning off the lighting. By adjusting the position of the plastic film, images with varying levels of illumination were acquired. The dataset is randomly divided into training, validation, and testing sets with a ratio of 8:1:1.

Funke, I. et al. Generative adversarial networks for specular highlight removal in endoscopic images. In Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10576, 8–16 (SPIE, 2018).

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Although these methods can remove highlights, they have many shortcomings: (i) they are only effective under specific conditions, thus limiting their practicality in different environments and materials; (ii) when the image has complex textures or the highlight area is too large, they will encounter significant challenges and cannot Remove highlights completely.

• Samples - any objects that intersect the light beam volume cast shadows through it. You may have witnessed this when sunlight shining through the clouds creates streaks of light, sometimes referred to as "god rays". The Sample setting defines the degree of accuracy that Modo uses to calculate these shadows through the volume; higher numbers of samples give smoother and more accurate results, while lower values render more quickly but become increasingly grainy. The example images below demonstrate the difference between 32 samples, on the left, and 256 samples on the right.

Shafaghi, H. et al. A fast and light fingerprint-matching model based on deep learning approaches. J. Signal Process. Syst. 95, 551–558 (2023).

The clients use local data and initial global parameters to train the local AttGAN for certain steps of gradient updates and then communicate the trained local parameters to the server.

where act is the activation function. The output \(\textbf{A}_{out}\) is used as the input for feature extraction of the next convolutional layer to enhance the feature extraction effect.

The network architecture is divided into three principal sub-networks: the highlight detection network (\({\text{H}}\)), the highlight removal network (\({\text{R}}\)), and the discriminator (\({\text{G}}\)). \({\text{H}}\) utilizes a fully convolutional architecture, featuring three layers each of downsampling and upsampling. Each downsampling layer is followed by two convolutional layers, while each upsampling layer is succeeded by three convolutional layers. \({\text{R}}\) is configured as an encoder-decoder network with skip connections, including two downsampling layers, one attention block to focus on salient features, four residual blocks to enhance feature propagation without loss, and two upsampling layers to reconstruct the output. The discriminator \({\text{G}}\) comprises a convolutional layer followed by five downsampling layers with a kernel size of 5 and stride of 2, with spectral normalization applied throughout to stabilize the training process.

Qiao, Z. et al. Seed: Semantics enhanced encoder-decoder framework for scene text recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 13528–13537 (2020).

Al-Ars, Z. et al. Almarvi system solution for image and video processing in healthcare, surveillance and mobile applications. J. Signal Process. Syst. 91, 1–7 (2019).

The server uses federated averaging algorithm to aggregate the local parameters to produce new global parameters for round \(t\in \left\{ 1,...,T\right\}\);

In encoder, the feature map \(\textbf{z}_l^{\textsf{T}}\) output by the l-th layer is input into the attention mechanism to mine the correlation in visual features. The attention mechanism mainly utilizes three learnable parameter matrices: \(\textbf{W}_Q\), \(\textbf{W}_K\) and \(\textbf{W}_V\). Initially, these matrices linearly convert the input \(\textbf{z}_l^{\textsf{T}}\) into corresponding attention feature matrices named \(\textbf{QX}\), \(\textbf{KX}\), and \(\textbf{VX}\).

• Enable - toggling this option off temporarily disables the targeting function while retaining the link between the items.

Following the standard convolutional neural network, each downsampling layer of the discriminator consists of a convolution module, batch normalization, and activation function. The downsampling layer reduces the spatial dimension of the image through convolution operations, which helps the model capture more abstract and high-level features. Batch normalization is applied to standardize the activation values of each layer’s input, which not only accelerates the training process but also enhances model stability and mitigates the issue of covariate shift. We use Leaky ReLU as the activation function. Different from the standard ReLU activation function, it allows a non-zero gradient when the input is negative, which can promote the activation of neurons.

Cai, S. et al. Gan-based image-to-friction generation for tactile simulation of fabric material. Comput. Graph. 102, 460–473 (2022).

Zhu, T. et al. Highlight removal in facial images. In Pattern Recognition and Computer Vision: Third Chinese Conference, PRCV 2020, Nanjing, China, October 16–18, 2020, Proceedings, Part I 3 422–433 (Springer, 2020).

He, C. et al. Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects. Preprint at http://arxiv.org/abs/2308.03166 (2023).

(3) discriminator: Based on the patchGAN structure, our discriminator \({\text{G}}\) focuses on discriminating local regions (“patch”) of the image rather than the entire image. This approach allows the discriminator to more precisely distinguish the differ between the generated highlight-free image \(\hat{\textbf{{I}}}{^{n}}\) and real highlight-free image \({\textbf{{I}}}{^{n}}\), enhancing the model’s sensitivity to details.

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• Render - allows you to select from three choices: when set to Default, you can enable/disable lights using the visibility function of the item list. When the light is visible, it contributes to the final rendered scene and, when invisible, it does not. On some instances you may prefer to fix this state, setting the light as On (enabled) or Off (disabled) regardless of visibility. Also useful for workflows that auto toggle visibility, saving you from manually enabling lights for test renders.

Specular highlight removal ensures the acquisition of high-quality images, which finds its important applications in stereo matching, text recognition and image segmentation. In order to prevent the leakage of images containing personal information, such as identification card (ID) photos, clients often train specular highlight removal models using local data resulting in a lack of precision and generalization of the trained model. To address this challenge, this paper introduces a new method to remove highlight in images using federated learning (FL) and attention generative adversarial network (AttGAN). Specifically, the former builds a global model in the central server and updates the global model by aggregating model parameters of clients. This process does not involve the transmission of image data, which enhances the privacy of clients; the later combining attention mechanisms and generative adversarial network aims to improve the quality of highlight removal by focusing on key image regions, resulting in more realistic and visually pleasing results. The proposed FL-AttGAN method is numerically evaluated, using SD1, SD2 amd RD datasets. The results show that the proposed FL-AttGAN outperforms existent methods.

• Scale - allows you to numerically set the size of the light item. This scale transform is a multiplier of the Height and Width options.

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We train the FL-AttGAN network for highlight removal across 10 local devices, configuring the system to operate with local epochs \(E=3\). The learning rates \(\alpha\), \(\beta\), and \(\gamma\) for the highlight detection network, highlight removal network, and discriminator, respectively, are all set to \(0.0001\), and a batch size of \(b=4\). Training extended over 10,000 communication rounds. Utilizing the TensorFlow framework, the network processes all images at a resolution of \(512 \times 512\).

Song, J. & Ye, J. C. Federated cyclegan for privacy-preserving image-to-image translation. Preprint at http://arxiv.org/abs/2106.09246 (2021).

Liu, G. & Guo, J. Bidirectional lstm with attention mechanism and convolutional layer for text classification. Neurocomputing 337, 325–338 (2019).

Before presenting FL-AttGAN, we first define relevant notations and preprocess the RGB images and friction coefficients. We consider a set of clients \(c\in \left\{ 1,2,\dots ,C\right\}\) and use \(\mathscr {D}^c\) to denote the local dataset of client c. The complete dataset containing all surface material data is composed of data from each client c, which can be denoted by \(\mathscr {D}\triangleq \bigcup _c{\mathscr {D}^c}\).

He, C. et al. Weakly-supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grouping. Adv. Neural Inf. Process. Syst. 36, 1 (2024).

This iterative process ensures that the model is collaboratively trained across all clients without sharing sensitive local data, reducing the risk of data breaches.Further detailed procedure of FL-AttGAN is presented in Algorithm 1.

• LPE Label - you can provide a label name here, which appears as the label for any Light Path Expression output you generate for this light. See Rendering with mPath.

In addition to privacy concerns, the accuracy of the highlight removal model on each client also needs to be improved. Recently, the deep learning method11,12 has been well developed for removing highlight, but they encounter the following challenge: existent methods have insufficient characterization capabilities. Tacking generative adversarial network (GAN)13 as an example, it cannot completely eliminate highlights, resulting in the loss of key information of the original image. To address this problem, we integrate an attention mechanism into the GAN-based framework. This enhancement allows the model to focus more effectively on highlighted areas and their surrounding detailed features. The attention mechanism captures spatial contextual information, focuses precisely on highlighted areas and effectively removes them while preserving the original details of the image.

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Given this setup, we suppose that N pairs of images are collected, including images with specular highlight \({\mathscr {S}} {\hspace{1.0pt}} = \{\textbf{S}{^{1}}{,} \ldots ,{\textbf{S}}{^{N}}\}\), the corresponding highlight-free images \({\mathscr {I}}{\hspace{1.0pt}} {\hspace{1.0pt}} = \{\textbf{I}{^{1}}, \ldots ,\textbf{I}{^{N}}\}\) and binary mask images indicating the location of highlight \({\mathscr {M}}{\hspace{1.0pt}} {\hspace{1.0pt}} = \{\textbf{M}{^{1}}{,} \ldots ,{\textbf{M}}{^{N}}\}\).

After T communication rounds, the server aggregates a global AttGAN model that is capable of generating highlight-free images.

FL is a new type of artificial intelligence (AI) that makes great contributions to data privacy and security by keeping data localized, reducing attack vectors, and supporting advanced privacy-enhancing technologies9. A key feature of FL is that data remains on local devices, eliminating the need to transmit sensitive information over the network to a central server. This aligns closely with data protection regulations such as the General Data Protection Regulation (GDPR)24, which emphasize data minimization and localization. Unlike centralized data storage that is vulnerable to cyberattacks, distributed data storage under the FL framework makes such attacks less rewarding and more difficult. Furthermore, FL can integrate cutting-edge encryption techniques, including secure multi-party computation (SMPC) and homomorphic encryption, which ensure that model updates transmitted across the network remain secure. These methods prevent unauthorized access and eavesdropping, safeguarding the privacy of the data throughout the process.

Mothukuri, V. et al. A survey on security and privacy of federated learning. Future Gener. Comput. Syst. 115, 619–640 (2021).

LED spotlights

All data used in this study were sourced from publicly available databases13, and all information is generated by software, involving no real personal data. Therefore, there are no ethical or privacy concerns associated with this research.

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• Name - this data field displays the current item name. You can easily change it by clicking within the field and typing the new name.

• Visible to Reflection/Refraction Rays - you can enable either or both of these options to make the light itself visible to reflective and/or transparent surfaces. The size of the visible light is determined by the Radius attribute.

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In the era of unprecedented data growth, the demand for image data has surged across various sectors, particularly sensitive ones such as digital forensics1, medical diagnostics2, and surveillance3. However, the acquisition of high-quality images is consistently challenged by real-world complexities, such as highlight illumination, which can significantly degrade the quality in highlighted regions. This degradation adversely affects the performance of computer vision tasks that require high-quality inputs, such as stereo matching4, text recognition5,6 and image segmentation7,8. Consequently, developing models that effectively remove highlights is essential. Because image data frequently contains personal information (e.g., identification cards, IDs), safeguarding data privacy is of paramount importance. Limited by laws, regulations, trade secrets, and personal privacy, clients are constrained to use only local data to train their highlight removal models, leading to a “data island” phenomenon. Models trained in this manner not only lack generalization ability but also are less accurate due to the limited amount of training data.

• Zero - resets the chosen transform property values to '0', leaving the Center position and Mesh position intact. This is done by adding an additional transform item to the Mesh Item's channels with an inverted version of the current values. This is useful to allow, for example, a joint to have a base value of 0,0,0 but still be located away from the World Origin.

• Set Target - by selecting the Light item, and a single additional item in the item list, and then pressing Set Target, this function allows you to target specific items in a scene, automating the rotation of an item, so that it continuously points toward the targeted item. Once activated, additional options appear:

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This work was supported by the Guangdong Provincial Department of Education under the 2023 Key Research Projects for Ordinary Higher Education Institutions.

Furthermore, to simulate the multi-user highlight removal model training task via federated learning, we distributed the training samples across 10 devices. Each device was allocated a different number of images with highlights.

• Order - allows you to set the order that the rotations are applied to the light item. Changing the order that rotations are applied can sometimes help to reduce or eliminate "gimbal lock".

• Height - this is the depth of the volume, as measured extending out from the front of the light. A setting of 20m extends the rays 20m from the light, including falloff.

In this paper, we propose a highlight removal method based on federated learning (FL) and attention generative adversarial network (AttGAN). This method (FL-AttGAN) can provide an improved performance, since: (i) federated learning avoids the transfer of raw data, and constructs a global model solely through the aggregation of client’s model parameters, thereby protecting sensitive image data; (ii) the local AttGAN incorporates both attention mechanisms and adversarial strategies, which ensures that the local model effectively removes highlights as accurately as possible. The main contributions are summarized as follows:

• Radius - when above '0', gives the direct light some size. The default value of 0m results in crisp hard-edged shadows. An increased Radius produces a soft-edged result; the larger the Radius, the softer the specular highlight and shadow is. Keep in mind that increasing this value too high results in noisy shadows unless you also increases the number of Direct Illumination Light Samples in the Render item, or the light's Samples value when adaptive sampling is disabled.

In this study, we introduce a dual-stage framework designed to identify and eliminate specular highlights from text images. The complete architecture is depicted in Fig. 2.

Yang, J. et al. Transferable face image privacy protection based on federated learning and ensemble models. Complex Intell. Syst. 7, 2299–2315 (2021).

where i and j represent size of image, the \(\phi\) is the feature maps of pre-trained VGG and \(\psi (\cdot )=\phi (\cdot ){\phi (\cdot )}^\mathsf{{T}}\).

Barrachina, J. A. et al. Comparison between equivalent architectures of complex-valued and real-valued neural networks-application on polarimetric sar image segmentation. J. Signal Process. Syst. 95, 57–66 (2023).

As the size increases, additional samples are required to smooth out the resulting soft specular shading and soft edge shadows. If they appear grainy, increasing this sample setting is the most likely solution. The default value is a good starting point for balancing speed and quality. As the size increases or the resolution of the image increases you can drive this number higher to account for any grain that appears. Keep in mind that increasing the number of samples also increases render times.

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Furthermore, we conduct an evaluation of the proposed local highlight removal network AttGAN by comparing it to three existent methods using the SD1, SD2, and RD datasets. The first of these methods is the UNet method 28, the second is the CycleGAN method 29, and the third is the HQG-Net method 30. Additionally, to provide a comprehensive comparison, we evaluated the SSIM, PSNR, recall, and precision of highlight images (denoted as “Light”) on the same datasets, which serve as benchmarks. The results are presented in Table 2. From the PSNR and SSIM results, it can be observed that, compared to state-of-the-art methods, AttGAN effectively improves the quality of highlight-removed images. This improvement can be attributed to its specially designed network architecture, the attention mechanism for feature capture, and the enhanced quality of generated images achieved by the discriminator. Consequently, this leads to an improvement in the precision and recall of text recognition. However, the precision does not show a significant difference, which is likely due to the inherently high precision of PaddleOCR; once the text information is successfully detected and recalled, high-performance text recognition is achieved.

We perform a numerical evaluation on the proposed FL-AttGAN using the SD1, SD2 and RD datasets 13. The SD1 dataset comprises 3679 original images containing text from supermarkets and streets, while the SD2 dataset includes 2025 original images of identity and driver’s licenses, which contain extensive textual information that necessitates heightened privacy protection. Utilizing the 3D computer graphics software Blender’s Cycles rendering engine, 27,700 sets of images were automatically generated, each set featuring specular highlights, alongside corresponding non-specular images and specular masks. Highlight shapes such as circular, triangular, elliptical, and annular were included, with lighting intensities randomly set within the [40,70] range to simulate real-world lighting conditions. The CTPN text detection model identified text regions where specular highlights were specifically applied to emphasize the text areas in the images. The SD1 dataset randomly divides samples into training, validation, and testing sets in an 8:1:1 ratio. The SD2 dataset follows the same partitioning strategy.

• Time Offset - time the offset, by a number of frames, to set how the light follows the target item. It can either be delayed behind it with a negative value, or run ahead of it with a positive value.

(1) highlight detection network The highlight detection network \({\text{H}}\) processes the image \({{\textbf{S}}}{^{n}}\) with specular highlights, and generates a mask \(\hat{\textbf{{M}}}{^{n}}\) that identifies highlight regions, where \(n\in \{1,2,...,N\}\).

Meka, A. et al. Lime: Live intrinsic material estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 6315–6324 (2018).

Goddard, M. The eu general data protection regulation (gdpr): European regulation that has a global impact. Int. J. Mark. Res. 59, 703–705 (2017).

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The attention mechanism is a powerful concept in machine learning, particularly in the field of natural language processing and computer vision. At its core, the attention mechanism allows a model to focus on specific parts of the input data when making predictions, rather than processing all input data with equal importance. This selective focus enables the model to prioritize more relevant or important information, thereby improving the overall performance of tasks such as translation, image captioning, and text summarization.

Highlight detection methods have been well developed in recent years. Klinker et al. use the color space analysis to divide color pixels into diffuse pixels, highlight pixels and saturated pixels14. To separate these components, they explore convex polygon fitting techniques and establish a connection between the color space and the dichromatic illumination model (DIM), fitting the highlight component into the dichromatic plane. Based on this model, work15 proposes an analytical solution for highlight removal based on the \(\text{L}_2\) chromaticity definition and DIM. This method involves few complex calculations and therefore removes highlights from images quickly and efficiently. But these methods usually only work on a single image. Su et al. propose a method to remove specular highlights from multi-view facial images16. They exploit Lambertian consistency to provide non-negative constraints on light and shadow in all directions, and further facilitate highlight removal by using orthogonal subspace projections.

In recent years, many researchers have explored the potential of attention mechanisms to solve a variety of difficult problems, further extending their application in natural language processing (NLP), computer vision 20 and other fields. He et al. propose a feature decomposition and edge reconstruction model for camouflage object detection 21. The model utilizes a frequency attention module and a guide-based feature aggregation module to mine subtle cues that distinguish foreground and background. The work 22 proposes a novel end-to-end network with attention mechanism for automatic facial expression recognition. The introduction of the attention mechanism can make neural networks more focus on useful functions. In NLP, the work23 proposes a novel unified architecture that includes bidirectional long short-term memory, attention mechanism, and convolutional layer. The architecture captures the local features of the phrase as well as the global sentence semantics.

• Visible to Camera - you can enable this option to make the light itself visible to cameras. The size of the visible light is determined by the Radius attribute.

Although experiments have proven the feasibility of these methods, they still have problems that need to be solved: (i) current methods lack adequate characterization capabilities. That is to say, they fall short in fully removing highlights, leading to the omission of crucial details from the original image. (ii) the training of deep learning networks usually requires a large amount of data, but the data collected by each client is limited, resulting in the lack of generalization capabilities of the local model; (iii) there is a risk that images containing private information may be disclosed during the collection and processing.

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The detection loss is used to evaluate the error between the generated mask \(\hat{\textbf{{M}}}{^{n}}\) and true mask \({\textbf{{M}}}{^{n}}\).

The server initializes and defines the AttGAN model, which comprises a highlight detection network \({\text{H}}\) and highlight removal network \({\text{R}}\) and a discriminator \({\text{G}}\), and broadcasts the initial global network parameters \(\textbf{w}_{0,h}\), \(\textbf{w}_{0,r}\) and \(\textbf{w}_{0,g}\) to all participating.

(4) parameter determination: To optimize the performance of the highlight detection network, highlight removal network and discriminator, we consider a mini-batch of B and establish following loss functions as constraints.

The data that support the findings of this study are openly available in the public data repository at the website of SD1 (https://drive.google.com/file/d/1xpb7TbUC5JOSUhtOWdKmr2hG-VJLMIC3/view), SD2 (https://drive.google.com/file/d/1RuCd2MA_pVdQMu0NtzDCu1zCPNMsxDys/view) and RD (https://drive.google.com/file/d/1m1Ab8RMc1Booyizbyvgw8y8B709vfYeu/view).

Zhou, F., Ye, Y. & Song, Y. Image segmentation of rectal tumor based on improved u-net model with deep learning. J. Signal Process. Syst. 94, 1145–1157 (2022).

For the highlight removal network, we employ an encoder–decoder architecture as the core framework, with skip connections integrated between the encoder and the decoder. We add self-attention mechanism in the feature extraction stage of the encoder to help the model focus on highlight areas, which is particularly important to maintain the accuracy and authenticity of the generated images. The advanced encoder is responsible for capturing the information of the image, gradually reducing the spatial dimension and increasing the feature depth through a series of convolutional layers and pooling layers. The decoder increases the dimensionality of the input image features and gradually constructs a highlight-free image.