Sparse Optimisation Research Code

Scientific Computational Imaging COde

Python routines to compute the Total Variation (TV) of 2D, 3D and 4D images on CPU & GPU. Compatible with proximal algorithms (ADMM, Chambolle & Pock, ...)

Inpainting via convex optimization.

Algorithms for total variation denoising

A Bayesian framework for Multi-Frame Image Super-Resolution. Based on "Bayesian Image Super-Resolution" (ME Tipping and CM Bishop, NeurIPS 2003)

Cut-pursuit with preconditioned forward-Douglas-Rachford for regularization of classical functionals by graph total variation

[TVCG 2021] Mesh Total Generalized Variation for Denoising

UTV：A novel dark channel prior guided variational framework for underwater image restoration

Carpet: Neural Net based solver for the 1d-TV problem

Code for Adaptation Network introduced in "Block-wise Scrambled Image Recognition Using Adaptation Network" paper (AAAI WS 2020)

Primal-Dual Solver for Inverse Problems

Solving hyperbolic PDEs using the Lax-Wendroff Scheme and a finite volume method.

External Module for Total Variation Algorithms, providing wrap for https://github.com/albarji/proxTV

Deformable Groupwise Image Registration using Low-Rank and Sparse Decomposition

An Image Reconstructor that applies fast proximal gradient method (FISTA) to the wavelet transform of an image using L1 and Total Variation (TV) regularizations

Imaging Inverse Problems and Bayesian Computation - Python tutorials to learn about (accelerated) sampling for uncertainty quantification and other advanced inferences

Denoising based on TV used ADMM or Proximal Project or Primal Dual metohd.

Code for creating maximal activation images (like Deep Dream) in pytorch with various regularizations / losses.

Tomographic image reconstruction package in Julia