UGPNet: Universal Generative Prior for Image Restoration
_{Official PyTorch Implementation of the WACV 2024 Paper}

UGPNet: Universal Generative Prior for Image Restoration
Hwayoon Lee, Kyoungkook Kang, Hyeongmin Lee, Seung-Hwan Baek, Sunghyun Cho

[Paper] [Supple]

Environment Setting

conda env create -f environment.yaml
conda activate ugpnet
export BASICSR_JIT=True # We use basicsr library

Training

UGPNet consists of three modules, and each module is trained sequentially. When you execute the following commands, you can train UGPNet with NAFNet (deblur) using the provided pretrained weights. You can modify the arguments to train with your own dataset, model, and degradation. Please refer to the training codes.

1. Restoration module

    python train_resmodule.py

2. Synthesis module

    python train_synmodule.py

3. Fusion Module

    python train_fusmodule.py

Inference

We provide pretrained weights of UGPNet with NAFNet (denoising, deblurring) and UGPNet with RRDBNet (super-resolution) and some sample images. [Download]

• Image Denoising

    test_scripts/test_UGPNet_w_NAFNet_denoise.sh
  

• Image Deblurring

    test_scripts/test_UGPNet_w_NAFNet_deblur.sh
  

• Super-resolution X8

    test_scripts/test_UGPNet_w_RRDBNet_sr.sh