Sparse Optimisation Research Code
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Updated
Jan 17, 2025 - Python
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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.
Primal-Dual Solver for Inverse Problems
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)
An Image Reconstructor that applies fast proximal gradient method (FISTA) to the wavelet transform of an image using L1 and Total Variation (TV) regularizations
Denoising based on TV used ADMM or Proximal Project or Primal Dual metohd.
Deeply Learned Spectral Total Variation Decomposition.
Grid-free Frank-Wolfe algorithm for solving least squares problem regularized with the total (gradient) variation
OL: Code for "Hyperspectral Image Super-resolution via Multi-stage Scheme without Employing Spatial Degradation"
An unofficial TensorFlow implementation of Total Deep Variation model
Overcoming Measurement Inconsistency in Deep Learning for Linear Inverse Problems: Applications in Medical Imaging (ICASSP 2021)
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.
Partial Differential Equations (PDEs) and its application in Image Restoration
[m1ds][project] Hyperspectral unmixing with Poisson noise
image processing and pattern recognition
Experiments with using total variation regularization on the ABIDE fmri dataset.
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