On this page, you will find listed a series of implementations that were featured in the Nuit Blanche monthly reviews (through July 2013) starting September 2012 (and including also those featured in A Year in Reproducible Research in Compressive Sensing, Advanced Matrix Factorization and more from September 2011 to August 2012), and made available by their authors in the spirit of reproducible research.
This list will probably grow longer as I go back to include all the implementations I have listed on Nuit Blanche. One should also note that a majority of these implementations are also listed in the reference pages such as the Big Picture in Compressive Sensing for compressive sensing related matters, or in the Advanced Matrix Factorization Jungle for solvers related to matrix factorization and Randomized Numerical Linear Algebra.
Hardware
Compressive Sensing: Reconstruction and Acquisition
- Compressed sensing and Approximate Message Passing with spatially-coupled Fourier and Hadamard matrices
- Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification
- Robust Compressed Sensing and Sparse Coding with the Difference Map
- PC-SBL: Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals
- PYKSVD : A highly optimized, parallel implementation of the Batch-OMP version of the KSVD learning algorithm - implementation -
- Direct deconvolution of radio synthesis images using L1 minimisation
- Implementation: EM-NN-AMP: Recovering Linearly-Constrained Non-Negative Sparse Signals
- Robust Sparse Signal Recovery for Compressed Sensing with Sampling and Representation Uncertainties
- An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals
- OrderedLASSO : Statistical Estimation and Testing via the Ordered L1 Norm
- Parallel and distributed sparse optimization
- Super-resolution via transform-invariant group-sparse regularization
- Sparse Spikes Deconvolution Numerical Tour
- Compressed Sensing with Linear Correlation Between Signal and Measurement Noise
- Sparse Recovery of Streaming Signals Using l_1-Homotopy - implementation -
- SL0-mod: Surpassing the Theoretical L_1 norm phase transition in Compressive Sennsing by Tuning the Smoother l_0 Algorithm
- Enhanced Compressed Sensing Recovery with Level Set Normals
- PalBOMP/PolBOMP: Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation
- Sparsity Averaging Reweighted Analysis (SARA) - implementation -
- GAGA: GPU Accelerated Greedy Algorithms - implementation -
- Correcting Errors in Linear Measurements and Compressed Sensing of Multiple Sources - implementation -
- SPASH: Sparse Shape Reconstruction - implementation -
- Compressive Sensing for Spread Spectrum Receivers, Spectral Compressive Sensing with Polar Interpolation
- Improving Smoothed l0 Norm in Compressive Sensing Using Adaptive Parameter Selection
- General Matching Pursuit (GMP): Is Matching Pursuit Solving Convex Problems?
- Structure-Based Bayesian Sparse Reconstruction - implementation -
- Compressed Sensing with Correlation Between Measurements and Noise - implementation -
- Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices
- Adaptive Outlier Pursuit: Robust 1-bit Compressive Sensing
- Fast Marginalized Block SBL Algorithm
- Belief Propagation Reconstruction for Discrete Tomography
- #Python implementation of BSBL code family
- nGpFBMP: A Fast Non-Gaussian Bayesian Matching Pursuit Method for Sparse Reconstruction
- A hand-waving introduction to sparsity for compressed tomography reconstruction - python
- WSPGL1: Beyond L1 minimization for sparse signal recovery
- Dynamic Compressed Sensing (DCS) via Approximate Message Passing (AMP)
- L1 Homotopy: A MATLAB Toolbox for Homotopy Algorithms in L1 Norm Minimization Problems
- Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation
- KL1p : Sparse Recovery of Underdetermined Inverse Problems
- Spread spectrum magnetic resonance imaging - s2MRI
- On Variable Density Compressive Sampling
- Generalizing Compressed Sensing: GraSP implementation available
- High Speed Compressed Sensing Reconstruction in Dynamic Parallel MRI
- Beyond Sparsity: Signal Recovery in Compressed Sensing via Universal Priors
- EM-GM-AMP: An Algorithm for Sparse Reconstruction
- ProPPA: A Fast Algorithm for $\ell_1$ Minimization and Low-Rank Matrix Completion
- DORE j'ADORE: Sparse signal reconstruction via ECME hard thresholding
- Implementation of Mirror Prox: ℓ1 Minimization via Randomized First Order Algorithm
- Compressed-Sensing Recovery of Images and Video Using Multihypothesis Predictions
- AMP on fire! An ASPICS Matlab Implementation and a VSLI/FPGA implementation
- A* Orthogonal Matching Pursuit papers and code implementation
- Newer Versions: Verifiable and computable performance analysis of sparsity recovery, Subspace Methods for Joint Sparse Recovery
- Implementation of rONE-L1
- ASPICS: Applying Statistical Physics to Inference in Compressed Sensing
- Signal Space CoSaMP Toolbox
- Split Bregman
- Sensing with Local Geometric Features: Hardware and Implementation
YALL1
For earlier implementations see the Sparse recovery solvers of the Compressive Sensing Big Picture page.
NonLinear Compressive Sensing (1bit, quantization, nonlinear measurements....)
- Quantized Iterative Hard Thresholding: Bridging 1-bit and High-Resolution Quantized Compressed Sensing - implementation -
- Robust 1-bit compressive sensing using binary matching pursuit
- Sparse Signal Reconstruction from Quantized Noisy Measurements via GEM Hard Thresholding
- A hybrid optimization approach for vector quantization
Compressive Sensing: Block Sparsity
- Faster Block Sparse Bayesian Learning Implementations
- Sparse Bayesian Methods for Low-Rank Matrix Estimation and Bayesian Group-Sparse Modeling and Variational Inference
Compressive Sensing: Known Synthesis Dictionaries/Frames
- FLAGLET: Exact wavelets on the ball
- S2LET: A code to perform fast wavelet analysis on the sphere
- Spin Spherical Harmonic Transforms (SSHT v 1.0)
Compressive Sensing: Known Analysis Operator (TV,....)
Compressive Sensing: Analysis Operator Learning:
Compressive Sensing: Synthesis Dictionary Learning
- K-SVD/IPR: Learning Incoherent Dictionaries for Sparse Approximation using Iterative Projections and Rotations
- Improving Dictionary Learning: Multiple Dictionary Updates and Coefficient Reuse
- AMP: Assembly Matching Pursuit, Metagenomic units (MGUs) discovery through sequence-based dictionary learning
- Accelerated DSI with Compressed Sensing using Adaptive Dictionaries
- GONDOLA: Generative-Discriminative Basis Learning for Medical Imaging
- MASTeR: Motion-Adaptive Spatio-Temporal Regularization for Accelerated Dynamic MRI
- Lipid Suppression in CSI with Spatial Priors and Highly Undersampled Peripheral K-Space
- COSMOS: sparse modeling software
- Direct Optimization of the Dictionary Learning Problem
- Applying alternating direction method of multipliers for constrained dictionary learning
Other Norms
Blind Deconvolution
- Analysis Based Blind Compressive Sensing
- Robust Locally Linear Analysis with Applications to Image Denoising and Blind Inpainting
- Correcting Camera Shake by Incremental Sparse Approximation
- Blind Deconvolution using Convex Programming
- Bayesian Blind Deconvolution With General Sparse Image Priors
- Compressive Sensing Under Matrix Uncertainties and Calibration
Compressive Sensing and Matrix Factorization
Machine Learning
Machine Learning
- XNV: Correlated random features for fast semi-supervised learning
- A Note on k-support Norm Regularized Risk Minimization -implementation -
- Robust Classification using Structured Sparse Representation
- Random Projections for Support Vector Machines
- Scattering Representations for Recognition
Semi Definite Programming
Feature Learning and Reconstruction
- ScatNet: An implementation of Scattering Networks transforms and classification algorithms
- Scattering Representations for Recognition
- From Bits to Images: Inversion of Local Binary Descriptors
- Robust image reconstruction from multi-view measurements
- Implementation of Mallat's Scattering transform
- New Implementations of Mallat's Scattering transform
- FREAK implementation on Github (1-bit Quantized Difference of Gaussians Descriptor)
Subspace Clustering and Tracking
- MOUSSE: Multiscale Online Union of SubSpaces Estimation - implementation -
- Greedy Approach for Subspace Clustering from Corrupted and Incomplete Data - implementation -
- Inductive Sparse Subspace Clustering - implementation - and Greedy Feature Selection for Subspace Clustering
- Sparse Manifold Clustering and Embedding
- Sparse Subspace Clustering: Algorithm, Theory, and Applications
- Improving Noise Robustness in Subspace-based Joint Sparse Recovery
- GRASTA: Grassmannian Robust Adaptive Subspace Tracking Algorithm Implementation
Manifold Learning
- BiG-AMP: Bilinear Generalized Approximate Message Passing
- The Block-Coordinate Update method for Advanced Matrix Factorization
- HoRPCA : Robust Low-Rank Tensor Recovery: Models and Algorithms
- 1-Bit Matrix Completion
- Bayesian Robust Matrix Factorization for Image and Video Processing - implementation -
- Sparse Localized Deformation Components - implementation -
- Sparse and Functional Principal Components Analysis - implementation -
- HTOpt: Optimization on the Hierarchical Tucker manifold - applications to tensor completion
- SLRA: Structured Low-Rank Approximation as Optimization on a Grassmann Manifold
- Bayesian methods for gene expression factor analysis - implementation -
- Complex Matrix Factorization Toolbox
- Sparse Generalized PCA or why you can fuggedabout Sparse PCA
- RTRMC: Exploiting manifolds' smoothness in Low Rank problems
- DECOLOR: A challenger to PCP
- SubMF changes to DCF, something new in ReProCS and Martin Jaggi's thesis and code.
- Compressive MUSIC, Forward/Backward Compressive Subspace Fitting Implementations
- Bilateral Random Projections
- Real-Time Principal Component Pursuit
- Matrix ALPS
- SA-MUSIC Algorithm Implementation
- Incremented Rank PowerFactorization algorithm
- Iterative Estimation of Constrained Rank-One Matrices in Noise
- Some Software Packages for Partial SVD Computation
- Iterative Reweighted Algorithms for Matrix Rank Minimization
Matrix Factorization: Low Rank
- Optimally weighted recovery of a low-rank signal matrix from a high-dimensional signal-plus-noise matrix
- The Phase Transition of Matrix Recovery from Gaussian Measurements Matches the Minimax MSE of Matrix Denoising
- qGeomMC: A Quotient Geometric approach to low-rank Matrix Completion - implementation
- \Greedy Approach for Low-Rank Matrix Recovery - implementation -
- PDRank: Penalty Decomposition Methods for Rank Minimization (and more)
- Low Rank Approximation and Regression in Input Sparsity Time
- Low-rank matrix completion by Riemannian optimization: Implementation
- Sparse Bayesian Methods for Low-Rank Matrix Estimation and Bayesian Group-Sparse Modeling and Variational Inference
Matrix Factorization: Robust PCA
- ReProCS: Recursive Robust PCA or Recursive Sparse Recovery in Large but Structured Noise
- Unbiased Risk Estimates for Singular Value Thresholding and Spectral Estimators
Matrix Factorization: Matrix Completion
- qGeomMC: A Quotient Geometric approach to low-rank Matrix Completion - implementation
- Scaled gradients on grassmann manifolds for matrix Completion
- Adaptive Outlier Pursuit: Matrix Completion
That Netflix RMSE is way too low or is it ? ( Clustering-Based Matrix Factorization)- SVDFeature: A Toolkit for Feature-based Collaborative Filtering - implementation -
- A Probabilistic Approach to Robust Matrix Factorization
- Bilinear modelling via Augmented Lagrange Multipliers (BALM)
- ProPPA: A Fast Algorithm for $\ell_1$ Minimization and Low-Rank Matrix Completion
- MF: Efficient Matrix Completion with Gaussian Models
Matrix Factorization: NMF
- Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization
- See All by Looking at A Few: Sparse Modeling for Finding Representative Objects
- nimfa - A Python Library for Nonnegative Matrix Factorization Techniques
- Robustness Analysis of HottTopixx, a Linear Programming Model for Factoring Nonnegative Matrices
- Hott Topixx: Factoring nonnegative matrices with linear programs
- #Python NMF/NTF with beta divergence
Matrix Factorization: Sparse PCA
Matrix Factorization: Dictionary Learning:
- Improving Dictionary Learning: Multiple Dictionary Updates and Coefficient Reuse
- AMP: Assembly Matching Pursuit, Metagenomic units (MGUs) discovery through sequence-based dictionary learning
- Accelerated DSI with Compressed Sensing using Adaptive Dictionaries
- GONDOLA: Generative-Discriminative Basis Learning for Medical Imaging
- MASTeR: Motion-Adaptive Spatio-Temporal Regularization for Accelerated Dynamic MRI
- Lipid Suppression in CSI with Spatial Priors and Highly Undersampled Peripheral K-Space
- COSMOS: sparse modeling software
- Direct Optimization of the Dictionary Learning Problem
- Applying alternating direction method of multipliers for constrained dictionary learning
- SPAMS: SPArse Modeling Software
Blind Deconvolution
- Robust Locally Linear Analysis with Applications to Image Denoising and Blind Inpainting
- Correcting Camera Shake by Incremental Sparse Approximation
- Blind Deconvolution using Convex Programming
- Bayesian Blind Deconvolution With General Sparse Image Priors
Tensor
- GRASTACam OPEN CV and Q-TT Toolbox in Python
- Tensorlab and Complex Optimization Toolbox
- #Python NMF/NTF with beta divergence
- QTT format and the TT-Toolbox
Structure Discovery
Randomized Numerical Linear Algebra
Phase Retrieval
- Phase Retrieval from masked Fourier transforms
- Phase retrieval for imaging problems
- GESPAR: Efficient Phase Retrieval of Sparse Signals and QCS: Sparsity based sub-wavelength imaging) -implementations-
- Compressive Phase Retrieval via Generalized Approximate Message Passing
- CPRL – An Extension of Compressive Sensing to the Phase Retrieval Problem
- PhaseCut: Phase Recovery, MaxCut and Complex Semidefinite Programming
- Compressive Phase Retrieval Implementation
- Alternating Direction Methods for Classical and Ptychographic Phase Retrieval
- High-accuracy wave field reconstruction using Sparse Regularization
Linear Systems
Dynamical Systems
Distances
Computer Vision: Estimation, Detection
Python
- #Python implementation of BSBL code family
- #Python NMF/NTF with beta divergence
- A hand-waving introduction to sparsity for compressed tomography reconstruction - python
FFT:
Inpainting:
- Inpainting Algorithm on GitHub (TV-L2 denoising and inpainting)
- GOAL: Analysis Operator Learning and Its Application to Image Reconstruction
Bio
- Accurate Profiling of Microbial Communities from Massively Parallel Sequencing using Convex Optimization
- Quikr: a Method for Rapid Reconstruction of Bacterial Communities via Compressive Sensing. - implementation -
- Squeezambler: Distilled Single Cell Genome Sequencing and De Novo Assembly for Sparse Microbial Communities
- Bayesian methods for gene expression factor analysis - implementation -
- Compressive Genomics
Inverse Problems
- Finite rate of innovation based modeling and compression of ECG signals - implementation -
- FrameSense: Near-Optimal Sensor Placement for Linear Inverse Problems / Acoustic echoes reveal room shape - implementation -
- PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities - implementation-
- The Fukushima Inverse Problem
Other
- Embedding Multiclass Data Hiding in Compressed Sensing
- CO4OI: Convex Optimisation for Optical Interferometry
- NACHOS: Nearfield Acoustic Holography using sparsity and compressive sampling principles - implementation -
- Model-Based Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing - implementation -
- Rapid Characterization of FPGAs with Matrix Completion
- Collaborative Filtering via Group-Structured Dictionary Learning
- Sparse Representation-Based/Exemplar-Based methods for Noise Robust Automatic Speech recognition (ASR)
- Poisson noise reduction with non-local PCA
- Patch Foveation in Nonlocal Imaging
- VSNR: Variational Stationary Noise Remover
- Compressive and Noncompressive Power Spectral Density Estimation Software Package and The Continuous-Time Spectrally-Sparse (CTSS) Sampling Toolbox
CS: Sudoku using POCS and Sparsity, Theoretical CS Q&A, "they had no idea", some meetings.
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