Image deblurring and denoising with non-local regularization constraint

Abstract

In this paper, we investigate the use of the non-local means (NLM) denoising approach in the context of image deblurring and restoration. We propose a novel deblurring approach that utilizes a non-local regularization constraint. Our interest in the NLM principle is its potential to suppress noise while effectively preserving edges and texture detail. Our approach leads to an iterative cost function minimization algorithm, similar to common deblurring methods, but incorporating update terms due to the non-local regularization constraint. The dataadaptive noise suppression weights in the regularization term are updated and improved at each iteration, based on the partially denoised and deblurred result. We compare our proposed algorithm to conventional deblurring methods, including deblurring with total variation (TV) regularization. We also compare our algorithm to combinations of the NLM-based filter followed by conventional deblurring methods. Our initial experimental results demonstrate that the use of NLM-based filtering and regularization seems beneficial in the context of image deblurring, reducing the risk of over-smoothing or suppression of texture detail, while suppressing noise. Furthermore, the proposed deblurring algorithm with non-local regularization outperforms other methods, such as deblurring with TV regularization or separate NLM-based denoising followed by deblurring.