Nonlinear optimization considering target feature points for bitelecentric camera calibration: experimental study.

The telecentric camera has found extensive application in microscopy imaging due to its remarkable attributes of maintaining constant magnification and minimal distortion within its depth of field. In telecentric imaging technology, the accuracy of measurements frequently hinges upon the calibration precision of the telecentric camera. In real-world scenarios, the shallow depth of field characteristic of telecentric cameras often leads to out-of-focus targets during the capturing process, which in turn results in the inability to accurately extract pixel coordinates of feature points, making it difficult for optimization algorithms to converge to the optimal value. We propose a nonlinear optimization algorithm based on pixel coordinates of optimized feature points for bitelecentric cameras. Incorporating pixel coordinates into the optimization process yields the theoretically optimal solution based on bitelecentric camera model. The obtained pixel coordinates are used for second initial value estimation, followed by the optimization of all parameters. Compared to existing methods, the proposed approach significantly reduces reprojection errors under both blurry and clear target conditions. Experimental results demonstrate superior performance in processing blurry defocused images.