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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.

This paper describes the complete implementation of a blind image denoising algorithm, that takes any digital image as input. In a first step the algorithm estimates a Signal and Frequency Dependent (SFD) noise model. In a second step, the image is denoised by a multiscale adaptation of the Non-local Bayes denoising method. We focus here on a careful analysis of the denoising step and present a detailed discussion of the influence of its parameters. Extensive commented tests of the blind denoising algorithm are presented, on real JPEG images and on scans of old photographs. Source CodeThe source code (ANSI C), its documentation, and the online demo are accessible at the IPOL web page of this article 1 .

Digital images are matrices of equally spaced pixels, each containing a photon count. This photon count is a stochastic process due to the quantum nature of light. It follows that all images are noisy. Ever since digital images have existed, numerical methods have been proposed to improve the signal-to-noise ratio. Such ‘denoising’ methods require a noise model and an image model. It is relatively easy to obtain a noise model. As will be explained in the present paper, it is even possible to estimate it from a single noisy image.