An Analysis and Implementation of the BM3D Image Denoising Method

By Marc Lebrun. Image Processing On Line, 2012

368 citations recorded by the source corpus.

BM3D is a recent denoising method based on the fact that an image has a locally sparse representation in transform domain. This sparsity is enhanced by grouping similar 2D image patches into 3D groups. In this paper we propose an open-source implementation of the method. We discuss the choice of all parameter methods and confirm their actual optimality. The description of the method is rewritten with a new notation. We hope this new notation is more transparent than in the original paper. A final index gives nonetheless the correspondence between the new notation and the original notation.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/a6dedebf2c1c4539ecb32eaa13384d4e121b9de7

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In Computer Vision and Pattern Recognition

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Citations in the corpus

A Nonlocal Bayesian Image Denoising Algorithm (2013, methodology): “In particular, this can be done with TV-denoising [18], NL-means [6], BM3D [26], KSVD [27], and NL-Bayes [5].”
Noise2Self: Blind Denoising by Self-Supervision (2019, methodology): “The hyperparameters of these methods have a large effect on performance (Lebrun, 2012), and on a new dataset with an unknown noise distribution it is difficult to evaluate their effects in a principled way.”