For more information, see Lens Distortion Correction in the "C API Reference" section of VPI - Vision Programming Interface.

The main loop of Lens Distortion Correction uses Remap, therefore performance is dominated by it. Refer to Remap's performance tables.

Barrel distortionexamples

\begin{align*} L(\tilde{x},\tilde{y}) &= \frac{r_d}{r} \begin{bmatrix} \tilde{x} \\ \tilde{y} \end{bmatrix} \\ r_d &= M_1(\theta_d) \\ \theta_d &= \theta(1+ k_1\theta^2 + k_2\theta^4 + k_3\theta^6 + k_4\theta^8) \\ \theta &= \arctan(r) \\ r &= \sqrt{\tilde{x}^2 + \tilde{y}^2} \end{align*}

VPI uses the structure VPIPolynomialLensDistortionModel to store the distortion parameters, which eventually is used by the vpiWarpMapGenerateFromPolynomialLensDistortionModel to create a VPIWarpMap that undistorts the input image.

Pincushiondistortion lens

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Barrel distortioncorrection

VPI comes with functions that handle both polynomial and fisheye distortion models. These models are characterized by distortion coefficients and, in the case of fisheye lenses, the mapping type. The coefficients are unique for each lens and can either be supplied by the manufacturer or estimated by a lens calibration process.

Pincushionbarrel distortion lens

The Lens Distortion Correction algorithm is implemented by warping the distorted input image into a rectified, undistorted output image. It does so by performing the inverse transformation; i.e., for every pixel \((u,v)\) in the destination image, calculate the corresponding coordinate \((\check{u},\check{v})\) in the input image.

Barrel distortionminuslens

Barrel distortion lensnikon

VPI uses the structure VPIFisheyeLensDistortionModel to store the distortion parameters, which eventually is used by the vpiWarpMapGenerateFromFisheyeLensDistortionModel to create a VPIWarpMap that undistorts the input image.

Fisheye lens is an extremely wide angle lens that produces strong barrel distortion. One of its uses is to create wide panoramas.

\begin{align*} L_r(\tilde{x},\tilde{y}) &= \frac{1+k_1r^2+k_2r^4+k_3r^6}{1+k_4r^2+k_5r^4+k_6r^6} \begin{bmatrix} \tilde{x} \\ \tilde{y} \end{bmatrix}\\ r^2 &= \tilde{x}^2 + \tilde{y}^2 \end{align*}

The equations above assume a Pinhole Camera Model. In the diagram shown in the link, the input camera is assumed to be aligned with world coordinate frame, with origin at \(O = (0,0,0)\) and optical axis colinear with world's \(Z_w\) axis. The output camera's origin is located at \(F_c\) and optical axis along \(Z_c\). Taken together, this makes the matrix \([R|t]\) transform points from input's camera space into output's.

Fisheye lenses can be classified depending on the relationship between the angle of incident light and where it is recorded on the image, established by the mapping function \(M_f(\theta)\).

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For list of limitations, constraints and backends that implements the algorithm, consult reference documentation of the following functions:

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\begin{align*} s \begin{bmatrix} \tilde{x} \\ \tilde{y} \\ 1 \end{bmatrix} &= \begin{bmatrix} x \\ y \\ z \end{bmatrix} = \mathsf{P_{in}} \\ (x_d,y_d) &= L(\tilde{x}, \tilde{y}) \end{align*}

Barrel distortionglasses

For a complete example, consult the sample application Fisheye Distortion Correction. It implements the whole process of rectifying images captured by a fisheye lens, including the calibration process.

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These equations above assume that projection is a linear operation. In reality, this is hardly the case. Lens distortions make straight lines in the real world appear projected as bent in the captured image. In order to take this into account, the distortion model is applied to the ideal, distortion-free coordinates in input camera space corresponding to the output image pixel coordinate being rendered. The resulting coordinates are the actual projected position on the input image of the rendered pixel in the output image.

VPI provides functions that, together with Remap algorithm, perform image rectification. The input image can have some level of distortion caused by the camera lens. The end result is an undistorted image that can optionally be reprojected into a second camera to allow, for instance, realignment of input camera's optical axis. This makes it an important stage in certain computer stereo vision applications, such as depth estimation, where two cameras must have their optical axis level and parallel.

\[ s \begin{bmatrix} \check{u} \\ \check{v} \\ 1 \end{bmatrix} = \mathsf{K_{in}} \begin{bmatrix} x_d \\ y_d \\ 1 \end{bmatrix} \]

Barrel distortionphotography

Tangential distortion is defined by parameters \(p_1\) and \(p_2\) and is due to imperfect centering of the lens components and other manufacturing defects.

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Polynomial distortion model, also known as Brown-Conrady model, allows representing a broad range of lens distortions, such as barrel, pincushion, mustache, etc.

\begin{align*} L_t(\tilde{x},\tilde{y}) &= \begin{bmatrix} 2p_1\tilde{x}\tilde{y} + p_2(r^2+2\tilde{x}^2) \\ p_1(r^2+2\tilde{y}^2) + 2p_2\tilde{x}\tilde{y} \end{bmatrix} \\ r^2 &= \tilde{x}^2+\tilde{y}^2 \end{align*}

The distortion model is defined by a mapping function \(M_f(\theta)\) that depends on fisheye lens type, and coefficients \(k_1,k_2,k_3\) and \(k_4\) as follows: