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Image Super-Resolution with Non-Local Sparse Attention

This repository is for NLSN introduced in the following paper "Image Super-Resolution with Non-Local Sparse Attention", CVPR2021, [Link]

The code is built on EDSR (PyTorch) and test on Ubuntu 18.04 environment (Python3.6, PyTorch >= 1.1.0) with V100 GPUs.

Contents

  1. Introduction
  2. Train
  3. Test
  4. Citation
  5. Acknowledgements

Introduction

Both Non-Local (NL) operation and sparse representa-tion are crucial for Single Image Super-Resolution (SISR).In this paper, we investigate their combinations and proposea novel Non-Local Sparse Attention (NLSA) with dynamicsparse attention pattern. NLSA is designed to retain long-range modeling capability from NL operation while enjoying robustness and high-efficiency of sparse representation.Specifically, NLSA rectifies non-local attention with spherical locality sensitive hashing (LSH) that partitions the input space into hash buckets of related features. For everyquery signal, NLSA assigns a bucket to it and only computes attention within the bucket. The resulting sparse attention prevents the model from attending to locations thatare noisy and less-informative, while reducing the computa-tional cost from quadratic to asymptotic linear with respectto the spatial size. Extensive experiments validate the effectiveness and efficiency of NLSA. With a few non-local sparseattention modules, our architecture, called non-local sparsenetwork (NLSN), reaches state-of-the-art performance forSISR quantitatively and qualitatively.

Non-Local Sparse Attention

Non-Local Sparse Attention.

NLSN

Non-Local Sparse Network.

Train

Prepare training data

  1. Download DIV2K training data (800 training + 100 validtion images) from DIV2K dataset or SNU_CVLab.

  2. Specify '--dir_data' based on the HR and LR images path.

For more informaiton, please refer to EDSR(PyTorch).

Begin to train

  1. (optional) Download pretrained models for our paper.

    Pre-trained models can be downloaded from Google Drive

  2. Cd to 'src', run the following script to train models.

    Example command is in the file 'demo.sh'.

    # Example X2 SR
python main.py --dir_data ../../ --n_GPUs 4 --rgb_range 1 --chunk_size 144 --n_hashes 4 --save_models --lr 1e-4 --decay 200-400-600-800 --epochs 1000 --chop --save_results --n_resblocks 32 --n_feats 256 --res_scale 0.1 --batch_size 16 --model NLSN --scale 2 --patch_size 96 --save NLSN_x2 --data_train DIV2K

Test

Quick start

  1. Download benchmark datasets from SNU_CVLab

  2. (optional) Download pretrained models for our paper.

    All the models can be downloaded from Google Drive

  3. Cd to 'src', run the following scripts.

    Example command is in the file 'demo.sh'.

    # No self-ensemble: NLSN
# Example X2 SR
python main.py --dir_data ../../ --model NLSN  --chunk_size 144 --data_test Set5+Set14+B100+Urban100 --n_hashes 4 --chop --save_results --rgb_range 1 --data_range 801-900 --scale 2 --n_feats 256 --n_resblocks 32 --res_scale 0.1  --pre_train model_x2.pt --test_only

Citation

If you find the code helpful in your resarch or work, please cite the following papers.

@InProceedings{Mei_2021_CVPR,
    author    = {Mei, Yiqun and Fan, Yuchen and Zhou, Yuqian},
    title     = {Image Super-Resolution With Non-Local Sparse Attention},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {3517-3526}
}
@InProceedings{Lim_2017_CVPR_Workshops,
  author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
  title = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {July},
  year = {2017}
}

Acknowledgements

This code is built on EDSR (PyTorch) and reformer-pytorch. We thank the authors for sharing their codes.