Abstract
The present paper is concerned with exploiting an iterative decoupling algorithm to address the problem of third-order tensor deblurring. The regularized deblurring problem, which is mathematically given by the sum of a fidelity term and a regularization term, is decoupled into an observation fidelity and a denoiser model steps. One basic advantage of the iterative decoupling algorithm is that the deblurring problem is supervised by the efficiency of the denoiser model. Thus, we consider a patch-based weighted low-rank tensor with sparsity prior. Numerical tests to image deblurring are given to demonstrate the efficiency of the proposed decoupling based algorithm.
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