Shrinking Gradient Descent Algorithms For Total Variation based Image Denoising
نویسندگان
چکیده
In this paper, we make some observations on the chambolle’s algorithm and projected gradient (GP) algorithm for the dual model of total variation denoising problems and propose a shrinking gradient descent algorithm (SGDA). We consider two frameworks, SGDA-1 and SGDA-2, according to the choice of shrinkage factor and step length. Global convergence analysis of these two frameworks are present. Numerical experiments indicate the new algorithms perform well in gray and color image denoising problems and the efficiency of SGDA is very competitive to chambolle’s algorithm and GP.
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تاریخ انتشار 2016