DAT_2_X2

Official paper pretrain model

SRFormer: Permuted Self-Attention for Single Image Super-Resolution

Yupeng Zhou 1, Zhen Li 1, Chun-Le Guo 1, Song Bai 2, Ming-Ming Cheng 1, Qibin Hou 1

1TMCC, School of Computer Science, Nankai University

2ByteDance, Singapore

arXiv

The official PyTorch implementation of SRFormer: Permuted Self-Attention for Single Image Super-Resolution (arxiv). SRFormer achieves state-of-the-art performance in

    classical image SR
    lightweight image SR
    real-world image SR

The results can be found here.

    Abstract: In this paper, we introduce SRFormer, a simple yet effective Transformer-based model for single image super-resolution. We rethink the design of the popular shifted window self-attention, expose and analyze several characteristic issues of it, and present permuted self-attention (PSA). PSA strikes an appropriate balance between the channel and spatial information for self-attention, allowing each Transformer block to build pairwise correlations within large windows with even less computational burden. Our permuted self-attention is simple and can be easily applied to existing super-resolution networks based on Transformers. Without any bells and whistles, we show that our SRFormer achieves a 33.86dB PSNR score on the Urban100 dataset, which is 0.46dB higher than that of SwinIR but uses fewer parameters and computations. We hope our simple and effective approach can serve as a useful tool for future research in super-resolution model design. Our code is publicly available at https://github.com/HVision-NKU/SRFormer.

Contents

    Installation & Dataset
    Training
    Testing
    Results
    Pretrain Models
    Citations
    License and Acknowledgement

Installation & Dataset

    python 3.8
    pyTorch >= 1.7.0

cd SRFormer
pip install -r requirements.txt
python setup.py develop

Dataset

We use the same training and testing sets as SwinIR, the following datasets need to be downloaded for training.
Task 	Training Set 	Testing Set
classical image SR 	DIV2K (800 training images) or DIV2K +Flickr2K (2650 images) 	Set5 + Set14 + BSD100 + Urban100 + Manga109
lightweight image SR 	DIV2K (800 training images) 	Set5 + Set14 + BSD100 + Urban100 + Manga109
real-world image SR 	DIV2K (800 training images) +Flickr2K (2650 images) + OST (10324 images for sky,water,grass,mountain,building,plant,animal) 	RealSRSet+5images
Training

    Please download the dataset corresponding to the task and place them in the folder specified by the training option in folder /options/train/SRFormer
    Follow the instructions below to train our SRFormer.

# train SRFormer for classical SR task
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_SRx2_scratch.yml
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_SRx3_scratch.yml
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_SRx4_scratch.yml
# train SRFormer for lightweight SR task
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_light_SRx2_scratch.yml
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_light_SRx3_scratch.yml
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_light_SRx4_scratch.yml

Testing

# test SRFormer for classical SR task
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_DF2Ksrx2.yml
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_DF2Ksrx3.yml
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_DF2Ksrx4.yml
# test SRFormer for lightweight SR task
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_light_DIV2Ksrx2.yml
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_light_DIV2Ksrx3.yml
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_light_DIV2Ksrx4.yml

Results

We provide the results on classical image SR, lightweight image SR, realworld image SR. More results can be found in the paper. The visual results of SRFormer will upload to google drive soon.

Classical image SR

    Results of Table 4 in the paper

    Results of Figure 4 in the paper

Lightweight image SR

    Results of Table 5 in the paper

    Results of Figure 5 in the paper

Model size comparison

    Results of Table 1 and Table 2 in the Supplementary Material

Realworld image SR

    Results of Figure 8 in the paper

Pretrain Models

Pretrain Models can be download from google drive. To reproduce the results in the article, you can download them and put them in the /PretrainModel folder.
Citations

You may want to cite:

@article{zhou2023srformer,
  title={SRFormer: Permuted Self-Attention for Single Image Super-Resolution},
  author={Zhou, Yupeng and Li, Zhen and Guo, Chun-Le and Bai, Song and Cheng, Ming-Ming and Hou, Qibin},
  journal={arXiv preprint arXiv:2303.09735},
  year={2023}
}

License and Acknowledgement

This project is released under the Apache 2.0 license. The codes are based on BasicSR, Swin Transformer, and SwinIR. Please also follow their licenses. Thanks for their awesome works.

SRFormerLight_SRx2_DIV2K

[Swift-SRGAN - Rethinking Super-Resolution for real-time inference](https://github.com/Koushik0901/Swift-SRGAN)

In recent years, there have been several advancements in the task of image super-resolution using the state of the art Deep Learning-based architectures. Many super-resolution-based techniques previously published, require high-end and top-of-the-line Graphics Processing Unit (GPUs) to perform image super-resolution. With the increasing advancements in Deep Learning approaches, neural networks have become more and more compute hungry. We took a step back and, focused on creating a real-time efficient solution. We present an architecture that is faster and smaller in terms of its memory footprint. The proposed architecture uses Depth-wise Separable Convolutions to extract features and, it performs on-par with other super-resolution GANs (Generative Adversarial Networks) while maintaining real-time inference and a low memory footprint. A real-time super-resolution enables streaming high resolution media content even under poor bandwidth conditions. While maintaining an efficient trade-off between the accuracy and latency, we are able to produce a comparable performance model which is one-eighth (1/8) the size of super-resolution GANs and computes 74 times faster than super-resolution GANs.

NOTE: The author used the wrong file extensions for the models *on GitHub*. You will download a `.pth.tar` file. This is not actually a TAR file. Change the file extension to just `.pth` and the model will work.

Swift-SRGAN 2x

Official 2x pretrain for [SPAN](https://github.com/hongyuanyu/span).

spanx2_ch48

Official DAT2 4x Pretrain of Dual Aggregation Transformer for Image Super-Resolution DAT

Transformer has recently gained considerable popularity in low-level vision tasks, including image super-resolution (SR). These networks utilize self-attention along different dimensions, spatial or channel, and achieve impressive performance. This inspires us to combine the two dimensions in Transformer for a more powerful representation capability. Based on the above idea, we propose a novel Transformer model, Dual Aggregation Transformer (DAT), for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner. Specifically, we alternately apply spatial and channel self-attention in consecutive Transformer blocks. The alternate strategy enables DAT to capture the global context and realize inter-block feature aggregation. Furthermore, we propose the adaptive interaction module (AIM) and the spatial-gate feed-forward network (SGFN) to achieve intra-block feature aggregation. AIM complements two self-attention mechanisms from corresponding dimensions. Meanwhile, SGFN introduces additional non-linear spatial information in the feed-forward network. Extensive experiments show that our DAT surpasses current methods.

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