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[ Tutorial on 2D wavelets ] [ WITS: Where is the starlet? ] | |
If you cannot find anything more, look for something else (Bridget Fountain) |
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Oversampled Filter Banks | Dual-Tree wavelets | FFT | Integer Bijections
Gene Regulatory Network Inference with clustering | Sparse Trend/Background/Baseline | Sparse Blind Deconvolution
Filter Bank generation (FB_gen) toolbox: Synthesis of Optimized Oversampled Inverse Filter Banks, given "almost any" real or complex analysis oversampled filter bank this toolbox offers some tools to build and optimize real or complex inverse oversampled filter banks. | |
Sure-LET denoising toolbox: this toolbox performs denoising of images using the FB-SURE-LET-S and FB-SURE-LET-C methods, with two-dimensional oversampled, directional filter banks and Stein Unbiaised Risk Estimation for LInear Expansion of Threshold. Some functions require the FB_gen toolbox | |
COLT toolbox: Complex Oversampled Lapped Transform toolbox for time-frequency analysis/synthesis and spectrogram processing (coming in 2019?) |
Matlab codes were created to illustrate the results presented in some of Jérôme Gauthier papers on optimization of multirate "oversampled filter banks" for denoising and image analysis purposes. You can use them freely for research purposes, as long as the following paper is credited (successfully tested with Matlab 2007b for windows):
Optimization of Synthesis Oversampled Complex Filter Banks (DOI:10.1109/TSP.2009.2023947, HAL)
Jérôme Gauthier, Laurent Duval and Jean-Christophe Pesquet
IEEE Transactions on Signal Processing, October 2009, Volume 57, Issue 10, p. 3827-3843
[MatlabCentral: M-band 2D dual-tree (Hilbert) wavelet multicomponent image denoising]+[Local Matlab version]+[precompiled coded version] |
Matlab codes were created to illustrate the results presented in some of Caroline Chaux papers. You can use them freely for research purposes, as long as the following papers are credited (successfully tested with Matlab 2007b for windows):
A nonlinear Stein-based estimator for multichannel image denoising (DOI:10.1109/TSP.2008.921757, Arxiv )
Caroline Chaux, Laurent Duval, Amel Benazza-Benyahia and Jean-Christophe Pesquet
IEEE Transactions on Signal Processing, August 2008, Volume 56, Issue 8, p. 3855-3870
Noise covariance properties in dual-tree wavelet decompositions (DOI:10.1109/TIT.2007.909104 )
Caroline Chaux, Jean-Christophe Pesquet and Laurent Duval
IEEE Transactions on Information Theory, December 2007, Volume 53, Issue 12, p. 4680-4700
Image analysis using a dual-tree M-band wavelet transform (DOI:10.1109/TIP.2006.875178)
Caroline Chaux, Laurent Duval and Jean-Christophe Pesquet
IEEE Transactions on Image Processing, August 2006, Volume 15, Issue 8, p. 2397-2412
Amplitude corrected m-file for computing/displaying the FFT of real signals |
Three different bijections or pairing functions between N and N2 (including Cantor polynomials)
Bijection_Pairing_N_N2(index_In,flag_Pair) provides three different explicit bijections between [0,...,K] and some consistently growing (Cantor or triangle, Elegant or square, Power-Of-Two-Odd or POTO for 2-adic integer decomposition) subset of N2. It allows different strategies to wander across a set of two-dimensional integer coordinates. |
BRANE Cut: Biologically-Related Apriori Network Enhancement with Graph cuts for Gene Regulatory Network Inference |
BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement |
Chromatogram baseline estimation and denoising using sparsity (BEADS) (on background estimation or baseline removal for analytic chemistry signals) http://lc.cx/beads |
Chromatogram baseline estimation and denoising using sparsity (BEADS) (DOI:10.1016/j.chemolab.2014.09.014)
Xiaoran Ning, Ivan Selesnick, Laurent Duval
Chemometrics and Intelligent Laboratory Systems, p. 156-167, Volume 139, December 2014This paper jointly addresses the problems of chromatogram baseline correction and noise reduction. The proposed approach is based on modeling the series of chromatogram peaks as sparse with sparse derivatives, and on modeling the baseline as a low-pass signal. A convex optimization problem is formulated so as to encapsulate these non-parametric models. To account for the positivity of chromatogram peaks, an asymmetric penalty functions is utilized. A robust, computationally efficient, iterative algorithm is developed that is guaranteed to converge to the unique optimal solution. The approach, termed Baseline Estimation And Denoising with Sparsity (BEADS), is evaluated and compared with two state-of-the-art methods using both simulated and real chromatogram data. See paper page
SOOT: Sparse blind deconvolution with Smooth l_1/l_2 norm (Smooth-One-Over-Two) ratio http://lc.cx/soot |
Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed l_1/l_2 Regularization (or SOOT, for Smoothed One-Over-Two norm ratio) (DOI:10.1109/LSP.2014.2362861)
Audrey Repetti, Mai Quyen-Pham, Laurent Duval, Émilie Chouzenoux, Jean-Christophe Pesquet
IEEE Signal Processing Letters, May 2015The l1/l2 ratio regularization function has shown good performance for retrieving sparse signals in a number of recent works, in the context of blind deconvolution. Indeed, it benefits from a scale invariance property much desirable in the blind context. However, the l1/l2 function raises some difficulties when solving the nonconvex and nonsmooth minimization problems resulting from the use of such a penalty term in current restoration methods. In this paper, we propose a new penalty based on a smooth approximation to the l1/l2 function. In addition, we develop a proximal-based algorithm to solve variational problems involving this function and we derive theoretical convergence results. We demonstrate the effectiveness of our method through a comparison with a recent alternating optimization strategy dealing with the exact l1/l2 term, on an application to seismic data blind deconvolution.