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The inherent physical limitations of imaging sensors lead to prevalence of additive white Gaussian noise in images which deters the feature extraction and analysis. There exists a number of denoising algorithms in literature, demonstrating their efficacy for removing noise while preserving feature details. At the crossing of functional and statistical analysis, one argues with new methods being devised quite frequently, whether the decade old BM3D is still efficient or not. While carrying out extended experimentation and evaluation for removal of Gaussian noise from natural images in terms peak signal to noise ratio, an argument in favor of BM3D has been presented in this manuscript.

BM3D Outperforms Major Benchmarks in Denoising: An Argument in Favor