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If you cannot find anything more, look for something else (Bridget Fountain) 


Oversampled Filter Banks  DualTree 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.  
SureLET denoising toolbox: this toolbox performs denoising of images using the FBSURELETS and FBSURELETC methods, with twodimensional 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 timefrequency 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 JeanChristophe Pesquet
IEEE Transactions on Signal Processing, October 2009, Volume 57, Issue 10, p. 38273843
[MatlabCentral: Mband 2D dualtree (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 Steinbased estimator for multichannel image denoising (DOI:10.1109/TSP.2008.921757, Arxiv )
Caroline Chaux, Laurent Duval, Amel BenazzaBenyahia and JeanChristophe Pesquet
IEEE Transactions on Signal Processing, August 2008, Volume 56, Issue 8, p. 38553870
Noise covariance properties in dualtree wavelet decompositions (DOI:10.1109/TIT.2007.909104 )
Caroline Chaux, JeanChristophe Pesquet and Laurent Duval
IEEE Transactions on Information Theory, December 2007, Volume 53, Issue 12, p. 46804700
Image analysis using a dualtree Mband wavelet transform (DOI:10.1109/TIP.2006.875178)
Caroline Chaux, Laurent Duval and JeanChristophe Pesquet
IEEE Transactions on Image Processing, August 2006, Volume 15, Issue 8, p. 23972412
Amplitude corrected mfile for computing/displaying the FFT of real signals 
Three different bijections or pairing functions between N and N^{2} (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, PowerOfTwoOdd or POTO for 2adic integer decomposition) subset of N^{2}. It allows different strategies to wander across a set of twodimensional integer coordinates. 
BRANE Cut: BiologicallyRelated Apriori Network Enhancement with Graph cuts for Gene Regulatory Network Inference 
BRANE Clust: ClusterAssisted 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. 156167, 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 lowpass signal. A convex optimization problem is formulated so as to encapsulate these nonparametric 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 stateoftheart methods using both simulated and real chromatogram data. See paper page
SOOT: Sparse blind deconvolution with Smooth l_1/l_2 norm (SmoothOneOverTwo) ratio http://lc.cx/soot 
Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed l_1/l_2 Regularization (or SOOT, for Smoothed OneOverTwo norm ratio) (DOI:10.1109/LSP.2014.2362861)
Audrey Repetti, Mai QuyenPham, Laurent Duval, Émilie Chouzenoux, JeanChristophe 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 proximalbased 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.