Wiener filtering in 3D transform domain with block-matching

Wiener filtering approach. We develop an empirical Wiener filtering extension provided an estimate of the noise-free image is available, obtained from the hard-thresholding technique, for example.

Block-matching. We do block-matching among blocks extracted from the initial estimate. Thus, we replace the -distance measure with the normalized $L^{2}$ -norm of the difference of two blocks with subtracted means; the definition of becomes
Wiener filtering in local 3D transform domain.
- Create a 3D array of Wiener filter's attenuating coefficients
- Produce local estimates and their weight:in analogy to the hard-thresholding approach.

Wiener filtering approach. We develop an empirical Wiener filtering extension provided an estimate of the noise-free image is available, obtained from the hard-thresholding technique, for example.