A Relaxed Split Bregman Iteration for Total Variation Regularized Image Denoising
The split Bregman iteration is an efficient tool for solving the total variation regularized minimization problems and has received considerable attention in recent years. In denoising case, it can remove noise well, but fails to preserve textures efficiently. In this paper, we reinterpret the split Bregman iteration from the perspective of function matching, and reveal the reason why it can not preserve textures well. To improve the performance of texture preservation, we develop a relaxed split Bregman iteration for total variation regularized image denoising. Numerical results show that for partly textured images, the new method can remove noise in the non-textured region and preserve textures in the textured region adaptively, and therefore it can improve the result both visually and in terms of the peak signal to noise ratio efficiently.
KeywordsSplit Bregman Iteration Texture Preservation Function Matching
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