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Mohammad Al Fahim's Projects

calgary_midl icon calgary_midl

semi-self supervised learning apporch to solved MC-MRI Reconstruction Calgary challenge

cs5691-prml-assignments icon cs5691-prml-assignments

Assignment submission for the course Pattern Recognition and Machine Learning (CS5691) in the Spring 2021 Semester, under Prof. C Chandra Sekhar.

dagl icon dagl

Dynamic Attentive Graph Learning for Image Restoration, ICCV2021 [PyTorch Code]

direct icon direct

Deep learning framework for MRI reconstruction

fastmri icon fastmri

A large-scale dataset of both raw MRI measurements and clinical MRI images.

ganhacks icon ganhacks

starter from "How to Train a GAN?" at NIPS2016

hyper-gan icon hyper-gan

This repository is the official Tensorflow implementation of the MICCAI 2021 paper "A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation".

invertible_rim icon invertible_rim

Official Code for "Invert to Learn to Invert" that allows training of invertible networks without storing activations

ista-net-pytorch icon ista-net-pytorch

ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing, CVPR2018 (PyTorch Code)

kiu-net-pytorch icon kiu-net-pytorch

Official Pytorch Code of KiU-Net for Image/3D Segmentation - MICCAI 2020 (Oral), IEEE TMI

mridc icon mridc

Data Consistency Toolbox for Magnetic Resonance Imaging

reconformer icon reconformer

ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer

restormer icon restormer

[CVPR 2022--Oral] Restormer: Efficient Transformer for High-Resolution Image Restoration. SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.

sewar icon sewar

All image quality metrics you need in one package.

stargan-v2 icon stargan-v2

StarGAN v2 - Official PyTorch Implementation (CVPR 2020)

swinir icon swinir

SwinIR: Image Restoration Using Swin Transformer (official repository)

vision-transformer-papers icon vision-transformer-papers

This repository contains an overview of important follow-up works based on the original Vision Transformer (ViT) by Google.

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