Lens distortion correction based on one chessboard pattern image

doi:10.1007/s12200-015-0453-7

Front. Optoelectron.

2015, Vol. 8

Issue (3) : 319-328 https://doi.org/10.1007/s12200-015-0453-7

RESEARCH ARTICLE

Lens distortion correction based on one chessboard pattern image

Yubin WU,Shixiong JIANG,Zhenkun XU,Song ZHU,Danhua CAO(

)

School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China

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Abstract

This paper proposes a detection method of chessboard corner to correct camera distortions –including radial distortion, decentering distortion and prism distortion. This proposed method could achieve high corner detection rate. Then we used iterative procedure to optimize distortion parameter to minimize distortion residual. In this method, first, non-distortion points are evaluated by four points near image center; secondly, Levenberg-Marquardt nonlinear optimization algorithm was adopted to calculate distortion parameters, and then to correct image by these parameters; thirdly, we calculated corner points on the corrected image, and repeated previous two steps until distortion parameters converge. Results showed the proposed method by iterative procedure can make the impact of slight distortion around image center negligible and the average of distortion residual of one line is almost 0.3 pixels.

Keywords computer vision camera distortion distortion correction corner detection

Corresponding Author(s): Danhua CAO

Just Accepted Date: 04 June 2015 Online First Date: 30 June 2015 Issue Date: 18 September 2015

Cite this article:

Yubin WU,Shixiong JIANG,Zhenkun XU, et al. Lens distortion correction based on one chessboard pattern image[J]. Front. Optoelectron., 2015, 8(3): 319-328.

URL:

https://academic.hep.com.cn/foe/EN/10.1007/s12200-015-0453-7
https://academic.hep.com.cn/foe/EN/Y2015/V8/I3/319

Fig.1 (a) Barrel distortion image; (b) pincushion distortion image

Fig.2 Image with decentering distortion

Fig.3 Image with thin prism distortion

Fig.4 (a) Distortion image; (b) the dots are detected corners in image (a) and the circles are ideal points location.

Fig.5 Forward-mapping from distortion image to correction image

Fig.6 White rectangles are the detected corners: (a) the proposed method; (b) the OpenCV method

Fig.7 Origin image without distortion

Fig.8 (a) and (b) are images with different distortion parameters; (c) and (d) are corrected image by proposed method, which parameters are shown in Table 1

Tab.1 Compares the distortion between man-made parameters and the results of proposed method

Fig.9 Performance of the proposed method with the synthetic data, dots are distorted points and circles are non-distorted points. (a) The distorted image; (b) the corrected image by the proposed method

Fig.10 Compare ERROR of the proposed method with Gao’s and Brown’s method versus different noises levels

Fig.11 ERROR of different rotations around the z-axis of image versus noises levels

Fig.12 ERROR versus different rotation around the x-axis of image

Fig.13 (a) Pictures taken with 4 mm lens, image size is 752 ×480; (b) pictures is taken with 8 mm lens, image size is 640 ×480

Fig.14 (a) Corrected image taken with 4 mm lens; (b) corrected image taken with 8 mm lens

Tab.2 Experiment results with proposed method

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