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, ...)

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

Primal-Dual Solver for Inverse Problems

A small tool in python to read the bright-field image data and the phase image data recovered from a Digital holographic microscope (DHM) and segment the nuclei to calculate physical parameters like roughness and volume.

OL: Code for "Hyperspectral Image Super-resolution via Multi-stage Scheme without Employing Spatial Degradation"

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

Overcoming Measurement Inconsistency in Deep Learning for Linear Inverse Problems: Applications in Medical Imaging (ICASSP 2021)

Grid-free Frank-Wolfe algorithm for solving least squares problem regularized with the total (gradient) variation

Deeply Learned Spectral Total Variation Decomposition.

Partial Differential Equations (PDEs) and its application in Image Restoration

[m1ds][project] Hyperspectral unmixing with Poisson noise

Inpainting via convex optimization.

An unofficial TensorFlow implementation of Total Deep Variation model

image processing and pattern recognition

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

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

Experiments with using total variation regularization on the ABIDE fmri dataset.