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DiffBIR is a diffusion-based image restoration framework designed for blind image reconstruction. It utilizes generative diffusion priors to recover high-quality images from sources with unknown or complex degradations without requiring explicit degradation models. The system includes specialized models for face restoration, enabling the recovery of facial landmarks, textures, and backgrounds in degraded portraits. To support high-resolution outputs on hardware with limited memory, it employs a tiled image upscaler that divides images into smaller patches during sampling. The framework cover
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.
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön Department of Information Technology, Uppsala University
Zongsheng Yue, Chen Change Loy
The main features of zsyoaoa/difface are: Face Restoration, Image Restoration, Inverse Problems.
Projects with overlapping indexed features include: xpixelgroup/diffbir — DiffBIR is a diffusion-based image restoration framework designed for blind image reconstruction. It utilizes… nachifur/rddm — CVPR 2024: Residual Denoising Diffusion Models. algolzw/image-restoration-sde — Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön Department of Information Technology,… dps2022/diffusion-posterior-sampling. liturout/psld — The repository contains reproducible PyTorch source code of our paper Solving Linear Inverse Problems Provably via… igitugraz/weatherdiffusion — This is the code repository of the following paper to train and perform inference with patch-based diffusion models…