Source: http://www.cs.tut.fi/~foi/GCF-BM3D/
Timestamp: 2019-04-18 17:05:32+00:00

Document:
We propose a novel image denoising strategy based on an enhanced sparse representation in transform-domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g. blocks) into 3D data arrays which we call "groups".
Collaborative filtering is a special procedure developed to deal with these 3D groups. We realize it using the three successive steps: 3D transformation of 3D group, shrinkage of transform spectrum, and inverse 3D transformation. The result is a 3D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and at the same time it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions.
Because these blocks are overlapping, for each pixel we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy.
A significant improvement is obtained by a specially developed collaborative Wiener filtering.
We develop algorithms based on this novel denoising strategy. The experimental results presented here demonstrate that the developed methods achieve state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
The following software is available exclusively for non-profit education and scientific research. Any unauthorized use of the software for industrial or profit-oriented activities is expressively prohibited.
Please read the TUT limited license before you proceed with downloading any of the files.
Table 1. PSNR (dB) results of the proposed grayscale BM3D method; can be reproduced with BM3D.m in the provided MATLAB codes.
Table 2. PSNR (dB) results of the CBM3D method; can be reproduced with CBM3D.m in the provided MATLAB codes.
Table 3. PSNR (dB) results of the grayscale VBM3D method on test sequences that can be found in this package. Note, the noisy videos had not been clipped in the interval [0,255] in order to conform to the considered observation model. The number of frames of each sequence is denoted with the @ symbol.
Table 4. PSNR (dB) results of the grayscale VBM3D method on noisy videos that can be found in this package. The noisy videos are quantized to 8 bits in the range [0,255]. Note, the quantization is responsible for the marginal differences with the corresponding results in Table 3.
Shape-Adaptive Transforms Filtering (Pointwise SA-DCT algorithm).
M. Maggioni, G. Boracchi, A. Foi, and K. Egiazarian, “Video denoising using separable 4D nonlocal spatiotemporal transforms”, Proc. SPIE Electronic Imaging 2011, Image Processing: Algorithms and Systems IX, 7870-2, San Francisco (CA), USA, January 2011.
M. Mäkitalo and A. Foi, “Spatially adaptive alpha-rooting in BM3D sharpening”, Proc. SPIE Electronic Imaging 2011, Image Processing: Algorithms and Systems IX, 7870-39, San Francisco (CA), USA, January 2011.
A. Danielyan, A. Foi, V. Katkovnik, and K. Egiazarian, “Spatially adaptive filtering as regularization in inverse imaging: compressive sensing, upsampling, and super-resolution”, in Super-Resolution Imaging (P. Milanfar, ed.), CRC Press / Taylor & Francis, ISBN: 978-1-4398-1930-2, September 2010 Examples of super-resolution reconstruction as zipped Matlab MAT-files.
K. Dabov, Image and video restoration with nonlocal transform-domain filtering, Tampere University of Technology, Publication 909, ISBN 978-952-15-2421-9, September 2010.
A. Danielyan, A. Foi, V. Katkovnik, and K. Egiazarian, “Denoising of Multispectral Images via Nonlocal Groupwise Spectrum-PCA”, Proc. 5th European Conf. Colour in Graphics, Imaging, and Vision, 12th Int. Sym. Multispectral Colour Science, CGIV2010/MCS'10, pp. 261-266, Joensuu, Finland, June 2010.
A. Danielyan, V. Katkovnik, and K. Egiazarian, “Image deblurring by augmented Lagrangian with BM3D frame prior”, Proc. 2010 Workshop on Information Theoretic Methods in Science and Engineering, WITMSE 2010, Tampere, Finland, August 2010.
V. Katkovnik and K. Egiazarian, “Nonlocal image deblurring: variational formulation with nonlocal collaborative l₀-norm imaging”, Proc. Int. Workshop on Local and Non-Local Approx. in Image Process., LNLA 2009, pp. 46-55, Tuusula, Finland, August 2009.
A. Danielyan and A. Foi, “Noise variance estimation in nonlocal transform domain”, Proc. Int. Workshop on Local and Non-Local Approx. in Image Process., LNLA 2009, Tuusula, Finland, pp. 41-45, August 2009.
A. Danielyan, M. Vehviläinen, A. Foi, V. Katkovnik, and K. Egiazarian, “Cross-color BM3D filtering of noisy raw data”, Proc. Int. Workshop on Local and Non-Local Approx. in Image Process., LNLA 2009, Tuusula, Finland, pp. 125-129, August 2009.
V. Katkovnik “Nonlocal collaborative l₀-norm prior for image denoising”, in Festschrift in Honor of Jaakko Astola on the Occasion of his 60th Birthday, TICSP Series 47, pp. 305-319, 2009.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “A nonlocal and shape-adaptive transform-domain collaborative filtering”, Proc. Int. Workshop on Local and Non-Local Approx. in Image Process., LNLA 2008, Lausanne, Switzerland, August 2008.
V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola, “Nonparametric regression in imaging: from local kernel to multiple-model nonlocal collaborative filtering”, Proc. Int. Workshop on Local and Non-Local Approx. in Image Process., LNLA 2008, Lausanne, Switzerland, August 2008.
G. Boracchi and A. Foi, “Multiframe raw-data denoising based on block-matching and 3-D filtering for low-light imaging and stabilization”, Proc. Int. Workshop on Local and Non-Local Approx. in Image Process., LNLA 2008, Lausanne, Switzerland, August 2008.
A. Danielyan, A. Foi, V. Katkovnik, and K. Egiazarian, “Image and video super-resolution via spatially adaptive block-matching filtering”, Proc. Int. Workshop on Local and Non-Local Approx. in Image Process., LNLA 2008, Lausanne, Switzerland, August 2008.
A. Danielyan, A. Foi, V. Katkovnik, and K. Egiazarian, “Image Upsampling Via Spatially Adaptive Block-Matching Filtering”, Proc. 16th European Signal Process. Conf., EUSIPCO 2008, Lausanne, Switzerland, August 2008.
K. Dabov, A. Foi, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE Electronic Imaging '08, no. 6812-07, San Jose, California, USA, January 2008.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Joint image sharpening and denoising by 3D transform-domain collaborative filtering,” Proc. 2007 Int. TICSP Workshop Spectral Meth. Multirate Signal Process., SMMSP 2007, Moscow, Russia, September 2007.
K. Dabov, A. Foi, and K. Egiazarian, “Video denoising by sparse 3D transform-domain collaborative filtering,” Proc. 15th European Signal Processing Conference, EUSIPCO 2007, Poznan, Poland, September 2007.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space,” Proc. IEEE Int. Conf. Image Process., ICIP 2007, San Antonio, TX, USA, September 2007.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080-2095, August 2007.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising with block-matching and 3D filtering,” Proc. SPIE Electronic Imaging '06, no. 6064A-30, San Jose, California, USA, January 2006.

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