Awesome
OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal
Qiao Mo, Yukang Ding, Jinhua Hao, Qiang Zhu, Ming Sun, Chao Zhou, Feiyu Chen, Shuyuan Zhu
UESTC, Kuaishou Techonology
Official implement of OAPT in ECCV2024, which is a transformer-based network deigned for double (or multiple) compressed image restoration.
TODO List
- Submit to ARXIV
- Release main codes of models
- Release test codes
- Release training codes
Architecture
Pattern clustering & inv operation
Experimental results on gray double JPEG images
Visual results
Training details
| Model(Gray) | Params(M) | Multi-Adds(G) | TrainingSets | Pretrain model | iterations |
|---|---|---|---|---|---|
| SwinIR | 11.49 | 293.42 | DF2K | 006_CAR_DFWB_s126w7_SwinIR-M_jpeg10 | 200k |
| HAT-S | 9.24 | 227.14 | DF2K | HAT-S_SRx2 | 800k |
| ART | 16.14 | 415.51 | DF2K | CAR_ART_q10 | 200k |
| OAPT | 12.96 | 293.60 | DF2K | 006_CAR_DFWB_s126w7_SwinIR-M_jpeg10 | 200k |
Setup
This project is mainly based on swinir and hat. All the weights are put in 'Baidu Netdisk' and 'Gdrive'
The version of PyTorch we used is 1.7.0.
pip install -r requirements.txt
python setup.py develop
Test
CUDA_VISIBLE_DEVICES=0 python oapt/test.py -opt ./options/Gray/test/test_oapt.yml
Train
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=73 hat/train.py -opt options/Gray/train/train_oapt.yml --launcher pytorch