Dabov, K.[Kostadin],
Foi, A.[Alessandro],
Katkovnik, V.[Vladimir],
Egiazarian, K.O.[Karen O.],
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,
IP(16), No. 8, August 2007, pp. 2080-2095.
IEEE DOI
0709
BibRef
And:
Color Image Denoising via Sparse 3D Collaborative Filtering with
Grouping Constraint in Luminance-Chrominance Space,
ICIP07(I: 313-316).
IEEE DOI
0709
See also Comments on Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering.
BibRef
Dabov, K.[Kostadin],
Foi, A.[Alessandro],
Egiazarian, K.O.[Karen O.],
Video denoising by sparse 3D transform-domain
collaborative filtering,
EUSIPCO07VBM3D.
Extends the image denoising to video.
BibRef
0700
Mäkinen, Y.[Ymir],
Azzari, L.[Lucio],
Foi, A.[Alessandro],
Collaborative Filtering of Correlated Noise: Exact Transform-Domain
Variance for Improved Shrinkage and Patch Matching,
IP(29), 2020, pp. 8339-8354.
IEEE DOI
2008
BibRef
Earlier:
Exact Transform-Domain Noise Variance for Collaborative Filtering of
Stationary Correlated Noise,
ICIP19(185-189)
IEEE DOI
1910
Transforms, Noise reduction, Collaboration, Kernel, Correlation,
BM3D.
Image denoising, correlated noise, collaborative filtering,
noise power spectrum.
BibRef
Lebrun, M.[Marc],
An Analysis and Implementation of the BM3D Image Denoising Method,
Image Processing,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
1210
Code, Denoising.
See also Small Neural Networks can Denoise Image Textures Well: A Useful Complement to BM3D.
BibRef
Li, Y.[Ying_Jiang],
Zhang, J.W.[Jiang-Wei],
Wang, M.N.[Mao-Ning],
Improved BM3D denoising method,
IET-IPR(11), No. 12, Decmeber 2017, pp. 1197-1204.
DOI Link
1712
Block matching 3D denoising.
BibRef
Wang, Y.Q.[Yi-Qing],
A Note on the Size of Denoising Neural Networks,
SIIMS(9), No. 1, 2016, pp. 275-286.
DOI Link
1604
BibRef
Wang, Y.Q.[Yi-Qing],
Morel, J.M.[Jean-Michel],
Can a Single Image Denoising Neural Network Handle All Levels of
Gaussian Noise?,
SPLetters(21), No. 9, Sept 2014, pp. 1150-1153.
IEEE DOI
1406
See also SURE Guided Gaussian Mixture Image Denoising. Gaussian noise
BibRef
Wang, Y.Q.[Yi-Qing],
Small Neural Networks can Denoise Image Textures Well:
A Useful Complement to BM3D,
IPOL(6), 2016, pp. 1-7.
DOI Link
1601
See also Image denoising: Can plain neural networks compete with BM3D?.
See also Analysis and Implementation of the BM3D Image Denoising Method, Image Processing, An.
See also Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing, A.
BibRef
Djurovic, I.[Igor],
Combination of the adaptive Kuwahara and BM3D filters for filtering
mixed Gaussian and impulsive noise,
SIViP(11), No. 4, May 2017, pp. 753-760.
WWW Link.
1704
BibRef
Yang, D.,
Sun, J.,
BM3D-Net: A Convolutional Neural Network for Transform-Domain
Collaborative Filtering,
SPLetters(25), No. 1, January 2018, pp. 55-59.
IEEE DOI
1801
collaborative filtering, feedforward neural nets, grey systems,
image colour analysis, image denoising, image matching,
nonlocal methods
BibRef
Tibbs, A.B.[Alexander B.],
Daly, I.M.[Ilse M.],
Roberts, N.W.[Nicholas W.],
Bull, D.R.[David R.],
Denoising imaging polarimetry by adapted BM3D method,
JOSA-A(35), No. 4, April 2018, pp. 690-701.
DOI Link
1804
Image reconstruction-restoration, Polarimetry
BibRef
Balaji, L.,
Thyagharajan, K.K.,
An enhanced performance for H.265/SHVC based on combined AEGBM3D filter
and back-propagation neural network,
SIViP(12), No. 5, July 2018, pp. 809-817.
WWW Link.
1806
BibRef
Feng, Q.P.[Qin-Ping],
Tao, S.P.[Shu-Ping],
Xu, C.[Chao],
Jin, G.[Guang],
BM3D-GT&AD: an improved BM3D denoising algorithm based on Gaussian
threshold and angular distance,
IET-IPR(14), No. 3, 28 February 2020, pp. 431-441.
DOI Link
2002
BibRef
Ehret, T.[Thibaud],
Arias, P.[Pablo],
Implementation of VBM3D and Some Variants,
IPOL(11), 2021, pp. 374-395.
DOI Link
2112
Code, Viedo Denoise. VBM3D is an extension to video of the well-known
image denoising algorithm BMD3.
See also Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering. o
BibRef
Guo, Y.[Yu],
Davy, A.[Axel],
Facciolo, G.[Gabriele],
Morel, J.M.[Jean-Michel],
Jin, Q.Y.[Qi-Yu],
Fast, Nonlocal and Neural: A Lightweight High Quality Solution to
Image Denoising,
SPLetters(28), 2021, pp. 1515-1519.
IEEE DOI
2108
Noise reduction, Image restoration, Signal processing algorithms,
Training, Mobile handsets, Neural networks, Convolution, BM3D,
nonlocal methods
BibRef
Hara, K.,
Inoue, K.,
Urahama, K.,
Full-Reference Metric Adaptive Image Denoising,
ICIP19(2419-2423)
IEEE DOI
1910
Denoising parameter optimization, BM3D,
full-reference image quality, Kullback-Leibler divergence
BibRef
Xue, F.,
Li, J.,
Blu, T.,
An Iterative Sure-Let Deconvolution Algorithm Based on BM3D Denoiser,
ICIP19(1795-1799)
IEEE DOI
1910
Image deconvolution, Stein's unbiased risk estimate (SURE),
linear expansion of thresholds (LET), BM3D, finite-difference Monte-Carlo
BibRef
Feng, X.C.[Xiang-Chu],
Li, X.H.[Xiao-Hui],
Wang, W.W.[Wei-Wei],
Jia, X.X.[Xi-Xi],
Improvement of BM3D Algorithm Based on Wavelet and Directed Diffusion,
CMVIT17(28-33)
IEEE DOI
1704
Block-matching and 3D filtering. Denoising.
BibRef
Metzler, C.A.[Christopher A.],
Maleki, A.[Arian],
Baraniuk, R.G.[Richard G.],
BM3D-PRGAMP: Compressive phase retrieval based on BM3D denoising,
ICIP16(2504-2508)
IEEE DOI
1610
BibRef
Earlier:
BM3D-AMP: A new image recovery algorithm based on BM3D denoising,
ICIP15(3116-3120)
IEEE DOI
1512
Algorithm design and analysis.
Approximate Message Passing; Compressive Sensing; Denoising; Onsager
BibRef
Sarjanoja, S.,
Boutellier, J.,
Hannuksela, J.,
BM3D image denoising using heterogeneous computing platforms,
DASIP15(1-8)
IEEE DOI
1605
filtering theory
BibRef
Li, L.[Lin],
Wang, R.G.[Rong-Gang],
Wang, W.M.[Wen-Min],
Gao, W.[Wen],
A low-light image enhancement method for both denoising and contrast
enlarging,
ICIP15(3730-3734)
IEEE DOI
1512
BM3D; Contrast enhancement; adaptive denoising; dark channel; superpixel
BibRef
Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Restoration by Deconvolution, Blind Deconvolution .