BM4D_v2p3.zip_去噪_图像 去噪_图像去噪 matlab_图像去噪matlab_彩色图像去噪

------------------------------------------------------------------- BM4D software for volumetric data denoising and reconstruction Public release ver. 2.3 (5 March 2013) ------------------------------------------------------------------- Copyright (c) 2010-2013 Tampere University of Technology. All rights reserved. This work should be used for nonprofit purposes only. Authors: Matteo Maggioni Alessandro Foi BM4D web page: http://www.cs.tut.fi/~foi/GCF-BM3D ------------------------------------------------------------------- Contents ------------------------------------------------------------------- The package contains these files *) demo_denoising.m : denoising demo script *) demo_reconstruction.m : reconstruction demo script *) bm4d.m : BM4D volumetric denoising filter [1] *) sampling.m : 3-D sampling trajectories generator *) (i)msfft2.m : multi-slice 2-D FFT (inverse) transform *) (i)dct3.m : 3-D DCT (inverse) transform *) visualizeXsect.m : displays phantom cross-sections *) constantsSparseTraj3D.m : useful constants used by master scripts *) ssim_index3d.m : 3-D SSIM index [4,5] *) SheppLogan3D.mat : 3-D Shepp-Logan phantom *) Transforms.mat : Default Wavelet transforms [1] *) t1_icbm_normal_1mm_pn0_rf0.rawb : BrainWeb T1 phantom [3] ------------------------------------------------------------------- Installation & Usage ------------------------------------------------------------------- Unzip BM4D.zip (contains codes) in a folder that is in the MATLAB path. Execute the script "demo_reconstruction.m" to run the reconstruction demo, and execute the script "demo_denoising.m" to run a volumetric denoising demo. You can freely change the parameters involved in the filtering by modifying their initial value at the beginning of the master scripts. Comments will help you to understand their meaning. ------------------------------------------------------------------- Requirements ------------------------------------------------------------------- *) MS Windows 64 bit, Linux 64 bit or Mac OS X 64 bit *) Matlab v.7.1 or later with installed: -- Image Processing Toolbox (for visualization with "imshow") -- Wavelet Toolbox (only for non-default parameters in bm4d.m) *) VST framework for Rician-distributed data. Downloadable from http://www.cs.tut.fi/~foi/RiceOptVST/. Required for the denoising of Rician data, and for the reconstruction of phantom data with non-zero phase. ------------------------------------------------------------------- Change log ------------------------------------------------------------------- v2.3 (5 March 2014) ! minor bug fixes in bm4d function + handled case of estimated standard deviation sigma=0 v2.2.1 (2 March 2014) ! introduced warning in case of sigma<=0 in demo_denoising v2.2 (20 September 2013) . default wavelet transforms do not longer require the wavelet toolbox . optimized memory usage + improved demo_denoising script for Rician spatially varying noise v2.1 (25 July 2013) + parametrized thresholding type (hard or soft) ! volumetric inputs with depth lower than the depth of the cubes are correctly handled, the code scales nicely also for the particular case of 2-D inputs v2.0 (17 April 2012) + reconstruction of volumetric phantom data with non-zero phase from noisy and incomplete Fourier-domain (k-space) measurements + adaptive denoising for data corrupted by spatially varying noise [2] v1.0.1 (18 July 2011) ! fixed few typos, corrected lambda_thr4D in modified profile v1.0 (17 July 2011) + initial version ------------------------------------------------------------------- References ------------------------------------------------------------------- [1] M. Maggioni, V. Katkovnik, K. Egiazarian, A. Foi, "A Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction", IEEE Trans. Image Process., vol. 22, no. 1, pp. 119-133, Jan. 2013. doi:10.1109/TIP.2012.2210725 [2] M. Maggioni, A. Foi, "Nonlocal Transform-Domain Denoising of Volumetric Data With Groupwise Adaptive Variance Estimation", Proc. SPIE Electronic Imaging 2012, San Francisco, CA, USA, Jan. 2012 [3] R. Vincent, "Brainweb: Simulated brain database", online at http://mouldy.bic.mni.mcgill.ca/brainweb/, 2006. [4] Z. Wang, A. Bovik, H. Sheikh, E. Simoncelli, "Image quality assessment: from error visibility to structural similarity", IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, April 2004. [5] J. V. Manjon, P. Coupe, A. Buades, D. L. Collins, M. Robles, "New methods for MRI denoising based on sparseness and self-similarity", Medical Image Analysis, vol. 16, no. 1, pp. 18-27, January 2012 ------------------------------------------------------------------- Disclaimer ------------------------------------------------------------------- Any unauthorized use of these routines for industrial or profit- oriented activities is expressively prohibited. By downloading and/or using any of these files, you implicitly agree to all the terms of the TUT limited license, as specified in the document Legal_Notice.txt (included in this package) and online at http://www.cs.tut.fi/~foi/GCF-BM3D/legal_notice.html ------------------------------------------------------------------- Feedback ------------------------------------------------------------------- If you have any comment, suggestion, or question, please do contact Matteo Maggioni at matteo.maggioni<at>tut.fi