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De-Convolution of images with VAMP algorithm (based on a Bayesian approach)

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VAMP Deconvolution

Original release date : 12/5/17

Reference #1 : "Vector Approximate Message Passing" Authors : Philip Schniter, Sundeep Rangan, Alyson K. Fletcher Download : https://arxiv.org/abs/1610.03082

Reference #2 : "Generalized Expectation Consistent Signal Recovery for Nonlinear Measurements" Authors : Hengtao He, Chao-Kai Wen, Shi Jin Download : https://arxiv.org/abs/1701.04301

Contributors : Alexis Gladiline Bozio, Florent Krzakala, Anthony Maggs, Andre Manoel, Eric Tramel

Contents

CS_Demo_Vamp_on_convoluted_images.m : Deconvolution of a blurred and downsampled image with the VAMP algorithm with the use of the selective inversion method and BM3D denoiser

Demo_streaming_vamp.m : Deconvolution of a blurred and downsampled toy image (only 0 and 1) with the Vector Approximate Message Passing algorithm using the selected inversion method and by dividing the image by block

Demo_Super_resolution_VAMP.m : Super_resolution reconstruction with Vamp and the Generalized Expectation Consistent Signal Recovery for Nonlinear Measurements.


This repository includes the BM3D package. The latest versions of this package can be found at: http://www.cs.tut.fi/~foi/GCF-BM3D/

This repository is calling the Selinv implementation of the Selected Inversion algorithm, which computes the selected elements of the inverse of a general sparse symmetric matrix. The latest versions of this package can be found at: https://math.berkeley.edu/~linlin/software.html

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De-Convolution of images with VAMP algorithm (based on a Bayesian approach)

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