Awesome
MegCup 2022 Team Feedback
This repository is the 1st place solution (Team Feedback) in 2022 MegCup RAW image denoising. Here is the 3rd place solution, FeedBack-based Restormer.
Members
- Ling-Hao Han
- Zuo-Liang Zhu
- Weilei Wen
- Adviser : Chunle Guo
Overview
<div align="center"> <img src="figs/fig-1.jpg" width="782" height="435"> </div> <details><summary>Click for more illustration</summary> <div align="center"> <img src="figs/fig-2.jpg" width="782" height="435"> <img src="figs/fig-3.jpg" width="782" height="435"> <img src="figs/fig-4.jpg" width="782" height="435"> </div> </details>Environment
git clone https://github.com/hlh981029/megcup-feedback.git
cd megcup-feedback
conda env create -f environment.yaml
conda activate feedback
Dataset
Please download the dataset to ./data
,
and refer to options/feedback.yaml
to modify the data path.
|--data
|--competition_train_input.0.2.bin
|--competition_train_gt.0.2.bin
|--competition_test_input.0.2.bin
Evaluation
# evaluate on dataset
# log and config file will be saved to ./output/feedback
python test.py
# generate result bin file
# result will be saved to ./output/feedback/submit/model_best_result.bin
python test.py --submit
Acknowledgement
This project is based on Global2Local, Swin-Transformer, Restormer, and BasicSR.