提出一种更公平的图像逆问题求解方法，提升去噪与超分辨率效果。
Fair Primal Dual Splitting Method for Image Inverse Problems
- 将平滑项同时引入原问题和对偶子问题，实现更均衡的优化
- 理论证明全局收敛性并给出收敛速率，优于现有方法
- 适合图像恢复、超分辨率等任务，尤其在高精度需求场景中表现突出
图像逆问题在图像处理、超分辨率和计算机视觉等领域具有广泛应用，可建模为三函数复合优化问题，可用多种原对偶型方法求解。本文提出一种公平的原对偶算法框架，将平滑项不仅纳入原问题子问题，也引入对偶子问题。统一了全局收敛性分析，并建立了所提方法的收敛速率。在图像去噪和超分辨率重建任务上的实验表明，该方法优于当前最先进的技术。
原文摘要 · Abstract (English)
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.
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