Joseph Nelson. (May 15, 2020). When to Use Contrast as a Preprocessing Step. Roboflow Blog: https://blog.roboflow.com/when-to-use-contrast-as-a-preprocessing-step/

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When considering how to add contrast to images and why we add contrast to images in computer vision, we must start with the basics. What is contrast? How contrast preprocessing improve our models? When should we add contrast?

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Note contrast is the act of contrast adjustment (Adaptive histogram equalization, AHE)is largely 'spreading' darker pixels more evenly across the image. This example also introduces a fundamental concept in improving contrast: local equalization.

Consider two images of the same subject at the same time: the moon. Our image on the left is low contrast, while the image on the right is higher contrast.

Note: doing contrast in a single framework can be problematic if ever trying to migrate to another framework, as not all implementations are identical.

Admesy supplies a large range of sampling accessories for measurement devices such as spectrometers, colorimeters, and light meters as well as light sources. Within this range, there are various collimating lenses available depending on your application requirements. Lens systems are either connected directly to the measurement systems, or they can all be connected to Admesy’s robust custom optical fiber. Our lens accessories provide flexibility when using a measurement device for research and development, or when integrating it into production lines with limited space for positioning the equipment.

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In recent years new display technologies have emerged. OLED displays, TFT’s with quantum dot technology or laser projectors offer brighter and more saturated colors than possible before. To achieve consistent colors across different displays, colorimetric calibration of the displays is essential.

Collimating lenses can also be used in lighting measurement applications. In particular when measuring diffuse surfaces of light sources such as OLED panels. Similar parameters including spectral power distribution, color and flicker can be determined depending on the measurement device.

Resolution usually is expressed in terms of "line-pairs," that is, a black line and a white line of equal width per unit distance.

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A common task where contrast is lower than desired is in processing scanned documents. In the case of low contrast, it can be challenging to deduce faint letters for optical character recognition (OCR). Creating greater contrast between the letters and the background makes clearer edges. Note that the contrast change is not simply making the entire image darker: the white background is a nearly equal shade.

‘Collimating’ is the process of accurately aligning light or particles in a parallel fashion. For light measurement, this ensures that the light has minimal spread as it propagates. Collimating lenses are for examples attached to spectrometers, colorimeters or light meters to ensure that the light that enters the instrument is parallel and covers the relevant measurement area.

Also, analysis applications where for example (spectral) transmission data of translucent materials is important for managing production processes. The advantage of collimating lenses attached to light sources and/or spectrometers is that light enters the sample under one specific angle only, with almost no variation. Again, this contributes to stable and repeatable measurement results.

Typical applications for Admesy products with collimating lenses are found within display measurement applications. Collimating lenses are for example extremely useful when measuring displays, which includes color analysis, white point adjustment, flicker, response time, gamma, etc. The optics are designed in such a way that alignment of the measurement spot can be optimized with stable measurement data and high repeatability as a result. This all contributes to the development and production of high-quality displays.

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Critically, contrast does not apply a blanket filter to increased/decrease all pixels by, say, 20 percent brightness. Instead, pixels in an image are adjusted on a relative basis: darker pixels are "smoothed" across the entire image. (We'll see more on this later.)

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In general, if a problem is believed to have images of low contrast or a portion of images with saturated contrast, smoothing the contrast of the image with preprocessing is helpful.

Contrast, at its core, is the condition of observable difference(s). In images, this means we capture the subject's clear differences. In the most atomic terms, this means pixels vary widely from one another.

Recall the difference between preprocessing and augmentation: preprocessing images means all images in our training, validation, and test sets should undergo the transformations we apply. Augmentation only applies to our training set.

Contrast preprocessing can be implemented in many open source frameworks, like image contrast in TensorFlow, image contrast preprocessing in PyTorch, and adjusting image contrast in FastAI, and histogram equalization contrast in scikit-image.

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Another advantage to Roboflow is your preprocessing is constant across your dataset, including across model frameworks. This easier empower experimentation and sampling results.

In comparing our moon images, it isn't only that the image on the right looks more pleasing, it also would be easier for our neural networks to understand. Recall a fundamental tenant in computer vision (whether classification, object detection, or segmentation) is edge detection. When using contrast preprocessing, edges become clearer as neighboring pixel differences are exaggerated.

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Contrast adjustments like adaptive equalization take into account local portions of an image to prevent outcomes like the center middle image, and instead spread contrast changes more evenly across the whole image.

A collimating lens system is typically made up of a tube with one or more lenses. By selecting the right properties of the lens and focal distance, light can be collimated with high accuracy. A collimating lens can either be attached directly to the measurement device or via a fiber connection for remote sensing.