Video denoising by sparse 3D transform-domain collaborative filtering

By Kostadin Dabov, Alessandro Foi, Karen O. Egiazarian. European Signal Processing Conference, 2007

545 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/fabf5ee1fc8e7696a587040e26830a0b4b8cef26

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Video Denoising, Deblocking, and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms (2012, methodology): “We compare the proposed filtering algorithm against V-BM3D [11], as this represents the state of the art in video denoising and we refer the reader to [11] for comparisons with other methods that are less effective than VBM3D.”
The SURE-LET Approach to Image Denoising (2007, methodology): “1 the results we obtained by running the VBM3D(6) [140], which is not a wavelet-based solution, but a more sophisticated two-pass algorithm.”
Burst photography for high dynamic range and low-light imaging on mobile cameras (2016, methodology): “Our approach takes inspiration from frequency-domain video denoising techniques that operate on 3D stacks of matching images patches [Kokaram 1993; Bennett and McMillan 2005; Dabov et al. 2007a].”
NAS-DIP: Learning Deep Image Prior with Neural Architecture Search (2020, methodology): “(b) Results of image inpainting on the dataset provided by [33] (left) and image denoising on the BM3D dataset [18] (right).”
Minimum Noise Fraction versus Principal Component Analysis as a Preprocessing Step for Hyperspectral Imagery Denoising (2016, methodology): “By looking at the 2 tables, we can see that PCA+VBM3D always achieves the highest SNRs for a wide range of noise levels in both HSI data cubes tested in this study.”
Robust video denoising using low rank matrix completion (2010, methodology): “The results are compared to that of two existing video denoising meth- ods: one is the VBM3D method ([10]) using the authors’ executable code from their website; the other is the PCAbased method by ([28]). it is noted that a depth constrained patch matching is used in [28] to form stacks of patches ”
Phase-based video motion processing (2013, methodology): “We achieved the overall best performance with VBM3D applied to the motion-magniﬁed video (comparisons with all the denoising methods in pre-and post-processing are available in the supplementary material).”
Image Sequence Denoising via Sparse and Redundant Representations (2009, methodology): “Egiazarian (the authors of [17]) for their willingness to provide a working implementation of the SW3D algorithm, especially K.”
Computer Vision with a Single (Robust) Classifier (2019, background): “4 Super-Resolution Super-resolution refers to the task of recovering high-resolution images given their low resolution version [DFE07; BSH12].”
Image Synthesis with a Single (Robust) Classifier (2019, background): “4 Super-Resolution Super-resolution refers to the task of recovering high-resolution images given their low resolution version [DFE07; BSH12].”
Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion (2012, unknown): “D K-SVD and VBM3D [31]).”