An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration without Clean Data"

Lighthouse 2 framework for real-time ray tracing

Image Restoration Toolbox (PyTorch). Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, IMDN

DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

EXPERIMENTAL: Real-time path tracing on the web with three.js

Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018) (Matlab)

Tensorflow implementation of conditional variational auto-encoder for MNIST

CVPR2019 (oral) Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers (AdaFM). PyTorch implementation

PyTorch Implementation of Noise2Noise (Lehtinen et al., 2018)

Official Pytorch Implementation for Trinity of Pixel Enhancement: a Joint Solution for Demosaicing, Denoising and Super-Resolution

Fast glsl deNoise spatial filter, with circular gaussian kernel, full configurable

Speech Enhancement using Bayesian WaveNet

🧹 Formerly for binary classification with noisy labels. Replaced by cleanlab.

Sharpness-aware Low Dose CT Denoising Using Conditional Generative Adversarial Network

Iterative Residual Network for Deep Joint Image Demosaicking and Denoising

An unofficial implementation of CBDNet by PyTorch.

Multi-Dimension Modulation for Image Restoration with Dynamic Controllable Residual Learning

PyTorch Implementation of "Densely Connected Hierarchical Network for Image Denoising", CVPRW, NTIRE2019

An unofficial implementation of CBDNet by Tensorflow.

A Conditional Generative Adverserial Network (cGAN) was adapted for the task of source de-noising of noisy voice auditory images. The base architecture is adapted from Pix2Pix.

🔧🧠 MEEGkit: M/EEG processing toolkit in Python 🧠🔧

DVDnet: A Simple and Fast Network for Deep Video Denoising

Pytorch implementation for 'Interactive Reconstruction of Monte Carlo Image Sequences using a Recurrent Denoising Autoencoder' , https://research.nvidia.com/publication/interactive-reconstruction-monte-carlo-image-sequences-using-recurrent-denoising

Low-dose CT via Transfer Learning from a 2D Trained Network, In IEEE TMI 2018

Denoise of ECG signal with machine learning

The set of CPU/GPU optimised regularisation modules for iterative image reconstruction and other image processing tasks

A curated list of awesome Speech Enhancement papers, libraries, datasets, and other resources.

[Preprint] "Segmentation-Aware Image Denoising without Knowing True Segmentation"

A Library for Denoising Single-Cell Data with Random Matrix Theory

python wrapper for rnnoise library