Scientific Computational Imaging COde
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Updated
Jul 11, 2024 - Python
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, ...)
An efficient GPU-compatible library built on PyTorch, offering a wide range of proximal operators and constraints for optimization and machine learning tasks.
A Python convex optimization package using proximal splitting methods
CoCaIn BPG escapes Spurious Stationary Points
New Matrix Factorization Algorithms based on Bregman Proximal Gradient: BPG-MF, CoCaIn BPG-MF, BPG-MF-WB
Primal-Dual Solver for Inverse Problems
Implementation of "Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems"
A Python package which implements the Elastic Net using the (accelerated) proximal gradient method.
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