------------------------------------------------------------------- BM4D software for volumetric data denoising and reconstruction Public release ver. 3.2 (30 March 2015) ------------------------------------------------------------------- Copyright (c) 2010-2015 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] *) helper.m : various methods used by the demos *) 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, or execute the script "demo_denoising.m" to run a volumetric denoising demo. You can freely modify the parameters involved in the filtering at the beginning of each demo. ------------------------------------------------------------------- Requirements ------------------------------------------------------------------- *) MS Windows 64 bit, Linux 64 bit or Mac OS X 64 bit *) Matlab R2011b or later with installed: -- Image Processing Toolbox (only for visualization with "imshow") -- Signal Processing Toolbox (only for non-default transforms in BM4D) -- Wavelet Toolbox (only for non-default transforms in BM4D) ------------------------------------------------------------------- Change log ------------------------------------------------------------------- v3.2 (30 March 2015) ! fixed bug in Rician noise estimation v3.1.1 (11 December 2014) + different transforms can be used within the same cube ! fixed bug arising when filtering 2D data ! fixed bug in 3rd dimensional inverse transformation v3.1 (10 November 2014) . code optimization to speed up filtering v3.0 (5 November 2014) + introduced low complexity profile in BM4D + improved interface of BM4D function ! fixed bug in Wiener filtering under Rician noise and unknown sigma . removed dependencies with VST package v2.4 (24 October 2014) . faster rician denoising with noise estimation v2.3 (5 March 2014) + handled case of estimated standard deviation sigma=0 ! minor bug fixes in bm4d function v2.2.1 (2 March 2014) ! introduced error in case of sigma<=0 in demo_denoising v2.2 (20 September 2013) + improved demo_denoising script for Rician spatially varying noise . default wavelet transforms do not longer require the wavelet toolbox . optimized memory usage 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.maggionitut.fi