Fair Primal Dual Splitting Method for Image Inverse Problems
This work addresses image processing tasks for applications in image science, but it appears incremental as it builds on existing primal dual methods with a novel twist.
The paper tackles image inverse problems like denoising and super-resolution by proposing a fair primal dual algorithmic framework that incorporates smooth terms into both primal and dual subproblems, demonstrating superiority over state-of-the-art methods in experiments.
Image inverse problems have numerous applications, including image processing, super-resolution, and computer vision, which are important areas in image science. These application models can be seen as a three-function composite optimization problem solvable by a variety of primal dual-type methods. We propose a fair primal dual algorithmic framework that incorporates the smooth term not only into the primal subproblem but also into the dual subproblem. We unify the global convergence and establish the convergence rates of our proposed fair primal dual method. Experiments on image denoising and super-resolution reconstruction demonstrate the superiority of the proposed method over the current state-of-the-art.