Blur Compensation Weighted Calibration Algorithm for Stereo Vision

The focus settings and physical characteristics of lenses significantly affect distortion correction, particularly under blurred imaging conditions, thereby limiting the measurement accuracy of existing stereo vision systems. Moreover, traditional calibration processes for stereo vision systems are complex due to the need for precise focus distance adjustments and careful calibration board selection. In this paper, we propose a camera parametric model based on weighted radial constraints that accounts for both radial and non-central lens distortion. This model aims to improve calibration accuracy while simplifying the calibration workflow. Additionally, we introduce a stereo vision correction method that utilizes speckle patterns as calibration images and eliminates the requirement for precise camera focusing. This approach is more user-friendly than conventional planar chessboard techniques, as it avoids frequent lens adjustments for optimal focus. Experimental results demonstrate improvements in accuracy of 17.2% and 15.6% over Zhang’s and Tsai’s calibration methods, respectively.