Open-source alternatives to CSBDeep

The code and pre-trained models of the paper "Image Deblurring based on Diffusion Models" will be released in this repository.

Jiezhang Cao, Yue Shi, Kai Zhang, Yulun Zhang, Radu Timofte, Luc Van Gool

Official implementation of Paper "Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Model" (ECCV 2024)

SUPIR is an AI image upscaler and restoration system designed to remove artifacts and restore quality to real-world photographs. It functions as a diffusion-based image enhancer and restoration tool that uses large-scale model scaling to produce high-resolution results with photorealistic details. The system balances visual aesthetics with input fidelity, allowing for a trade-off between strict adherence to the original image and the overall visual appeal of the output. It leverages large-scale model inference to improve image clarity and maintain realistic details during the upscaling proces

[Paper](https://arxiv.org/abs/2304.01247) [Project Page](https://generativediffusionprior.github.io/)

(† Co-first Author, Corresponding Author)

SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training

This is the code repository of the following paper to train and perform inference with patch-based diffusion models for image restoration under adverse weather conditions.

Project Page | Paper | Personal HomePage

Paper | Project

1.Sichuan University, 2.Southwest Jiaotong University, 3.University of Electronic Science and Technology of China, 4.Shanghai Jiaotong University, 5.Megvii Technology