cszn/KAIR

Health v2 · maintenance only

Adoption not part of the score

3523 stars · 705 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch image restoration toolbox providing training and testing code for many restoration models including DnCNN, FFDNet, SRMD, USRNet, DPIR, BSRGAN, and SwinIR. It covers tasks such as denoising, super-resolution, deblurring, and demosaicing.

Use cases

• denoise noisy photos with a pretrained deep learning model
• upscale and super-resolve low-resolution images
• train an image restoration network in PyTorch
• deblur blurry images
• benchmark image restoration models like SwinIR and BSRGAN
• restore compressed JPEG images

When to choose

• you need reference implementations of well-known image restoration models
• you want to train or fine-tune denoising or super-resolution networks in PyTorch
• you are doing low-level vision research and need a common codebase

When to avoid

• you need a polished end-user photo editing app with a GUI
• you want one-click production deployment rather than research code
• your task is high-level vision like detection or segmentation

Facets

library · maturity active

image-processing machine-learning deep-learning computer-vision image-processing deep-learning machine-learning python cross-platform image-restoration denoising super-resolution deblurring pytorch swinir dncnn research-code gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="cszn/KAIR")