The Regularization and Deconvolution Algorithm Combining Salient Edges and Average Curvature in High-quality Visual Communication

Abstract

The reconstruction of high-quality images is of great significance in fields such as medicine, visual communication, and satellite imaging. In order to avoid the interference of subjective and objective factors on image details and information quality, and reduce the damage of noise to video images, a deconvolution algorithm combining important edges and average curvature regularization is proposed to achieve image deblurring processing. By designing deconvolution models, alternating optimization of auxiliary variables, average curvature filter constraints, and mutual derivative image processing, image clarity can be improved. The proposed method was tested and the results showed that the success rate of deburring was higher than other comparative algorithms, and the repair results showed that the method can effectively achieve deblurring. This optimization method has good convergence during the iteration process, with peak signal-to-noise ratio, structural similarity index measurement value, and error ratio of 31.03, 0.96, and 1.61, respectively. The algorithm that considers edge information and curvature regularization processing can better preserve the quality and details of image information, which can effectively provide new technical means and tools for the field of visual communication.