Radial Distortion Correction of Grid Images Based on Support Vector Machines for Regression Using Particle Swarm Optimization Algorithm

Zheng, Yi; Zheng, Ping

doi:10.1109/icsip49896.2020.9339465

Cited by 1 publication

(1 citation statement)

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“…When the camera takes a picture, the object image gathers the light to the light sensor through the lens, and converts the light signal into an electrical signal to obtain a storable image. However, due to the deviation of manufacturing accuracy and assembly process of lens and sensor, the original image will be distorted, which will seriously affect the imaging accuracy of the instrument [1]. The lens shape deviation caused by the manufacturing process will gradually bend the light along the central position and cause radial distortion.…”

Section: Introductionmentioning

confidence: 99%

Distortion correction algorithm based on GPSO and improved LM

Chen¹,

Wang²

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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In optical cameras, there are deviations in the manufacturing process and assembly accuracy of image sensors, lenses and other related components, which will produce various forms of image distortions, seriously affecting the application and development of Computer Vision (CV) in real life. In order to solve these problems, a hybrid solution algorithm (GPSO-ILM) based on Global Particle Swarm Optimization (GPSO) and improved Levenberg-Marquardt (LM) is proposed on the basis of Homography Transformation, which transforms the solution of nonlinear distortion equations into a parameter optimization problem, and can quickly iterate to obtain the optimal solution of the target function. It can effectively avoid the problems that traditional LM depends on initial values and is easy to fall into local convergence. In order to verify the effectiveness of the algorithm, the improved GPSO-ILM method is compared with the Tasi method of two-steps and traditional Zhang's correction method in OpenCV. The experimental results show that the RMSE errors of the improved GPSO-ILM method are relatively reduced by more than 23% and 18%, and the processing time is saved by about 25%. Compared with other methods, this method has higher correction accuracy and faster processing efficiency.

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Section: Introductionmentioning

confidence: 99%