PSF-based restoration with edge adaptive regularization for structured light measurement

Edge information is important for measurement based on structured light. The presented effort puts forward an image restoration method with edge regularization for structured light measurement, which detects the edge of image, then updates the parameters of image degradation model and restores results in iteration. The diffraction limit of optics and nonlinear distortion of sensors are calculated as prior knowledge for semi-blind deconvolution. Blur metric is introduced for constraints of deconvolution iterations. Images before and after restoration are sent to shape recognition and automatic calibration modules for comparison. From experimental results we can conclude that the proposed approach can effectively enhance image quality and edge details, so that greater precision can be achieved.

► Image restoration is introduced to linear structured light measurement.
► PSF is set as prior information for blind deconvolution restoration.
► Canny and wavelet edge detections are used as constraints for regularization.
► Iteration time is determined by image evaluation including blur metric.