An Improved Optimal Proximal Gradient Algorithm for Non-Blind Image Deblurring
This work addresses a critical problem in image processing, specifically for applications requiring clearer visual representations, and presents an incremental improvement over existing deblurring algorithms.
The authors tackled the image deblurring problem and achieved enhanced image quality with improved PSNR and SSIM values. Their proposed algorithm, IOptISTA, outperformed existing methods with reduced tolerance.
Image deblurring remains a central research area within image processing, critical for its role in enhancing image quality and facilitating clearer visual representations across diverse applications. This paper tackles the optimization problem of image deblurring, assuming a known blurring kernel. We introduce an improved optimal proximal gradient algorithm (IOptISTA), which builds upon the optimal gradient method and a weighting matrix, to efficiently address the non-blind image deblurring problem. Based on two regularization cases, namely the $l_1$ norm and total variation norm, we perform numerical experiments to assess the performance of our proposed algorithm. The results indicate that our algorithm yields enhanced PSNR and SSIM values, as well as a reduced tolerance, compared to existing methods.