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 developTest
CUDA_VISIBLE_DEVICES=0 python oapt/test.py -opt ./options/Gray/test/test_oapt.ymlTrain
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