A fractional-order adaptive regularization primal-dual algorithm for image denoising
2015
This paper aims to develop a fractional-order model and a primal-dual algorithm for image denoising, where a regularization parameter can be adjusted adaptively according to Morozov discrepancy principle at each iteration to ensure that the denoised image retains in a specific set. In the light of saddle-point theory, the convergence of our proposed algorithm is guaranteed. Simulations with comparisons are carried out to demonstrate the effectiveness of our proposed algorithm for image denoising.
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