Video, Image and GIF upscale/enlarge(Super-Resolution) and Video frame interpolation. Achieved with Waifu2x, Real-ESRGAN, Real-CUGAN, RTX Video Super Resolution VSR, SRMD, RealSR, Anime4K, RIFE, IFRNet, CAIN, DAIN, and ACNet.

Real-time microphone noise suppression on Linux.

Recurrent neural network for audio noise reduction

Collection of popular and reproducible image denoising works.

QualityScaler - image/video AI upscaler app

Tensorflow 2.x implementation of the DTLN real time speech denoising model. With TF-lite, ONNX and real-time audio processing support.

PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

Lv2 suite of plugins for broadband noise reduction

The waifu2x & Other image-enlargers on Mac

Providing reproducibility in deep learning frameworks

Open Source Noise Cancellation App for Virtual Meetings

Official PyTorch Implementation of CleanUNet (ICASSP 2022)

A speech denoise lv2 plugin based on RNNoise library

RealScaler - image/video AI upscaler app (Real-ESRGAN)

Source code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio denoising methods by showing that it is possible to train deep speech denoisi…

Speech noise reduction which was generated using existing post-production techniques implemented in Python

Deep neural network (DNN) for noise reduction, removal of background music, and speech separation

Noise removal/ reducer from the audio file in python. De-noising is done using Wavelets and thresholding is done by VISU Shrink thresholding technique

A simple Python wrapper for audio noise reduction RNNoise. Simplifies work with it, adds new trained models and detailed instructions for training.

This is a RNNoise windows demo. It was modified and restructured so that it can be compiled with MSVC, VS2017, VS2019.