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.