MATLAB Implementation of Total Variation Image Denoising
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Resource Overview
Total Variation Image Denoising using Split Bregman Iterative Algorithm with L1, L2, and Lp Norms; Gaussian-Seidel Iteration and Shrinkage Operators Applied in L1 Model
Detailed Documentation
In this article, we focus on total variation image denoising methods. We implement the Split Bregman iterative algorithm combined with L1, L2, and Lp norms to optimize performance. For the L1 model implementation, we incorporate Gaussian-Seidel iteration for solving linear systems and shrinkage operators for efficient L1-norm minimization through thresholding operations. The algorithm handles image gradients using finite difference operators and employs auxiliary variables to separate the L1 regularization term. We conduct comparative analyses with other popular image denoising methods to demonstrate the superiority and practical applicability of our approach, including performance evaluations on standard test images with quantitative metrics like PSNR and SSIM.
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