Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction
Tampere University · Tampere University of Applied Sciences
Abstract
We present an extension of the BM3D filter to volumetric data. The proposed algorithm, BM4D, implements the grouping and collaborative filtering paradigm, where mutually similar d-dimensional patches are stacked together in a (d+1)-dimensional array and jointly filtered in transform domain. While in BM3D the basic data patches are blocks of pixels, in BM4D we utilize cubes of voxels, which are stacked into a 4-D "group." The 4-D transform applied on the group simultaneously exploits the local correlation present among voxels in each cube and the nonlocal correlation between the corresponding voxels of different cubes. Thus, the spectrum of the group is highly sparse, leading to very effective separation of…
Citation impact
- FWCI
- 17.48
- Percentile
- 100%
- References
- 40
Authors
4- MMMarco MaggioniCorresponding
Tampere University, Tampere University of Applied Sciences
- VKVladimir Katkovnik
Tampere University of Applied Sciences, Tampere University
- KEKaren Egiazarian
Tampere University, Tampere University of Applied Sciences
- AFAlessandro Foi
Tampere University of Applied Sciences, Tampere University
Topics & keywords
- Noise reduction
- Voxel
- Computer science
- Filter (signal processing)
- Pixel
- Noise (video)
- Artificial intelligence
- Algorithm