bm4d.m (13406, 2015-03-30)
bm4d_thr_mex.mexa64 (61377, 2015-03-30)
bm4d_thr_mex.mexmaci64 (52572, 2015-03-30)
bm4d_thr_mex.mexw64 (62976, 2015-03-30)
bm4d_wie_mex.mexa64 (59697, 2015-03-30)
bm4d_wie_mex.mexmaci64 (52460, 2015-03-30)
bm4d_wie_mex.mexw64 (66560, 2015-03-30)
demo_denoising.m (5703, 2015-03-30)
demo_reconstruction.m (19113, 2015-03-30)
helper.m (19783, 2015-03-30)
Legal_Notice.txt (2928, 2013-09-20)
SheppLogan3D.mat (262775, 2013-09-20)
ssim_index3d.m (6644, 2013-09-20)
t1_icbm_normal_1mm_pn0_rf0.rawb (7109137, 2014-03-02)
Transforms.mat (15658, 2014-11-04)
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BM4D software for volumetric data denoising and reconstruction
Public release ver. 3.2 (30 March 2015)
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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
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Contents
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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]
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Installation & Usage
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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.
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Requirements
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*) MS Windows *** bit, Linux *** bit or Mac OS X *** 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)
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Change log
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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
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References
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[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
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Disclaimer
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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
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Feedback
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If you have any comment, suggestion, or question, please do
contact Matteo Maggioni at matteo.maggioni
tut.fi