GPU-based distortion correction for CMOS positioning camera using star point measurement

Due to machining errors and assembly errors, the optical system of the camera is somewhat different from the ideal model through the pinholes. That means, there are different degrees of non-linear optical distortion between the ideal image and the actual image formed by the camera. In order to improve the positioning accuracy of the camera, distortion correction should be done. This paper puts forward a GPU-Based Star Calibration and Proofreading Algorithm (GBSC-PA). This algorithm is completed by CUDA programmable graphics processor technologies, and a number of lens distortion coefficients are solved using star point images. Moreover, the optimal distortion correction matrix can be used to improve the accuracy and speed of the algorithm greatly. Through the experimental measurements and simulation, the feasibility and efficiency of the new algorithm are verified. Compared with the conventional correction algorithm based on CPU, the speed-up ratio of GPU-based parallel program can be reached to 43.094.