BM3Dtype

BM3Dtype is a term used in image processing to describe a class of denoising algorithms that follow the Block-Matching and 3D Filtering (BM3D) framework. It is not a single standardized method, but a designation for methods that extend or adapt the core BM3D ideas to various data types, noise models, and application scenarios.

The common core of BM3D-type methods involves four steps. First, a search for similar blocks is performed within a defined neighborhood of each reference patch in the image. Second, the found blocks are grouped into a 3D stack, exploiting redundancy among similar patches. Third, a three-dimensional transform is applied to the stack, and the transform-domain coefficients are selectively shrinked or filtered, typically using hard-thresholding or Wiener-like rules. Fourth, the inverse transform is applied and the resulting patches are aggregated back into the image through weighted averaging to form the denoised output. Variants differ in details such as the similarity metric, the size of the search window, the block size, the chosen 3D transform, and the exact shrinkage or filtering rule.

BM3D-type methods have been applied to grayscale and color images, and in some cases extended to video and multi-spectral data, where correlations across color channels or frames are exploited either jointly or in staged processing. They are known for strong noise suppression with good preservation of edges and fine structures, but can be computationally intensive and sensitive to parameter choices. The BM3D framework originated with the 2007 publication by Dabov, Katkovnik, Foi, and Egiazarian, and has influenced a wide range of subsequent denoising approaches.