MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data

Improving the portability and tractability of egocentric action recognition on EPIC-KITCHENS by learning with compressed measurements.

This folder contains the image processing algorithms of Compressed Sensing techniques.

Reconstruct high-dimensional spectral representations of tinnitus using reverse correlation

Code Repository for "Orthogonally Weighted Regularization for Rank-Aware Joint Sparse Recovery: Algorithm and Analysis" Authors: A. Petrosyan, K. Pieper, H. Tran

MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data

CoSaMP algorithm in Haskell

Fast and Efficient Data Science Techniques for COVID-19 Group Testing

R1magic: Compressive Sampling: Sparse signal recovery utilities

Undergraduate thesis on compressive sensing

Compressive sensing exploit the possibility to represent an image with a sparse representation.

This is a repository for CS4ML. It is a general framework for active learning in regression problems. It approximates a target function arising from general types of data, rather than pointwise samples.

My master's thesis with the title: Iteratively Reweighted Algorithms for Dynamic MRI. Pázmány Péter Catholic University, Budapest, Hungary. June 2020.

A library to build up lazily evaluated expressions of linear transforms for efficient scientific computing. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711022000218

Utilities for seed-independent multidimensional nonuniform sampling

Intro lecture notes on compressed sensing in Armenian\ Սեղմ նմուշառություն։ ծանոթագրություն

Pulse-stream models in time-of-flight imaging

Assignment solutions for the course CS754 Advanced Image Processing, Spring 2024 at IIT Bombay