This repository is an official implementation of the paper "CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution", CVPR, 2025.
- ✅ 2025-03-15: Release the supplementary material of our CATANet.😃
- ✅ 2025-03-13: Release the pretrained models and visual results of our CATANet.🤗
- ✅ 2025-03-12: arXiv paper available.
- ✅ 2025-03-09: Release the codes of our CATANet.
- ✅ 2025-02-28: Our CATANet was accepted by CVPR2025!🎉🎉🎉
Abstract: Transformer-based methods have demonstrated impressive performance in low-level visual tasks such as Image Super-Resolution (SR). However, its computational complexity grows quadratically with the spatial resolution. A series of works attempt to alleviate this problem by dividing Low-Resolution images into local windows, axial stripes, or dilated windows. SR typically leverages the redundancy of images for reconstruction, and this redundancy appears not only in local regions but also in long-range regions. However, these methods limit attention computation to content-agnostic local regions, limiting directly the ability of attention to capture long-range dependency. To address these issues, we propose a lightweight Content-Aware Token Aggregation Network (CATANet). Specifically, we propose an efficient Content-Aware Token Aggregation module for aggregating long-range content-similar tokens, which shares token centers across all image tokens and updates them only during the training phase. Then we utilize intra-group self-attention to enable long-range information interaction. Moreover, we design an inter-group cross-attention to further enhance global information interaction. The experimental results show that, compared with the state-of-the-art cluster-based method SPIN, our method achieves superior performance, with a maximum PSNR improvement of
$\textbf{\textit{0.33dB}}$ and nearly$\textbf{\textit{double}}$ the inference speed.
⭐If this work is helpful for you, please help star this repo. Thanks!🤗
- Python 3.9
- PyTorch >=2.2
pip install -r requirements.txt
python setup.py develop- Download the training dataset DIV2K and put them in the folder
./datasets. - Download the testing data (Set5 + Set14 + BSD100 + Urban100 + Manga109 [Download]) and put them in the folder
./datasets. - It's recommended to refer to the data preparation from BasicSR for faster data reading speed.
- Refer to the training configuration files in
./options/trainfolder for detailed settings.
# batch size = 4 (GPUs) × 16 (per GPU)
# training dataset: DIV2K
# ×2 scratch, input size = 64×64,800k iterations
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --standalone --nnodes=1 --nproc_per_node=4 basicsr/train.py -opt options/train/train_CATANet_x2_scratch.yml
# ×3 finetune, input size = 64×64, 250k iterations
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --standalone --nnodes=1 --nproc_per_node=4 basicsr/train.py -opt options/train/train_CATANet_x3_finetune.yml
# ×4 finetune, input size = 64×64, 250k iterations
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --standalone --nnodes=1 --nproc_per_node=4 basicsr/train.py -opt options/train/train_CATANet_x4_finetune.yml- Download the testing data (Set5 + Set14 + BSD100 + Urban100 + Manga109 [Download]) and put them in the folder
./datasets.
- Download the pretrained models and put them in the folder
./pretrained_models.
- Refer to the testing configuration files in
./options/testfolder for detailed settings.
python basicsr/test.py -opt options/test/test_CATANet_x2.yml
python basicsr/test.py -opt options/test/test_CATANet_x3.yml
python basicsr/test.py -opt options/test/test_CATANet_x4.ymlPlease cite us if our work is useful for your research.
@article{liu2025CATANet,
title={CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution},
author={Xin Liu and Jie Liu and Jie Tang and Gangshan Wu},
journal={arXiv preprint arXiv:2503.06896},
year={2025}
}
If you have any questions, feel free to approach me at [email protected]
This code is built on BasicSR.