Dual Regression Compression (DRC)

Yong Guo, Jingdong Wang, Qi Chen, Jiezhang Cao, Zeshuai Deng, Yanwu Xu, Jian Chen, Mingkui Tan

This repository contains the official Pytorch implementation and the pretrained models of Towards Lightweight Super-Resolution with Dual Regression Learning.

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Contents

• Dual Regression Compression (DRC)
  • Contents
  • Catalog
  • Datasets
  • Models
  • Evaluating and Training
  • Results
  • Citation

Catalog

• We release the pre-trained models of some large SR models enhanced by our Dual Regression (DR).
• We release the pre-trained models of the non-blind SR models compressed by our Dual Regression Compression (DRC).
• We release the pre-trained models of the blind SR models compressed by our Dual Regression Compression (DRC).
• We release the code of evaluation and training.

Datasets

Used training and testing sets can be downloaded as follows:

Training Set	Non-Blind Testing Set	Blind Testing Set
DIV2K (800 training images, 100 validation images) + Flickr2K (2650 images)	You can evaluate our non-blind models on several widely used benchmark datasets, including Set5, Set14, B100, Urban100, Manga109.	You can evaluate our blind models on DIV2KRK dataset.

Please organize the datasets using the following hierarchy.

- datasets/
    - DIV2K
        - DIV2K_train_HR
        - DIV2K_train_LR_bicubic

    - DF2K
        - DF2K_HR
        - DF2K_LR_bicubic
    
    - benchmark
        - Set5
        - Set14
        - B100
        - Urban100
        - Manga109
    
    - DIV2KRK
        - gt
        - lr_x2 
        - lr_x4

Models

You can download the pre-trained large SR models enhanced by our Dual Regression (DR) scheme for 4x SR. More pretrained models can be found in the released assets of this repository.

Method	Params	FLOPs (G)	Dataset	PSNR (dB)	SSIM	Model Zoo
DRN-S	4.8M	109.9	Set5	32.68	0.901	Download
DRN-L	9.8M	224.8	Set5	32.74	0.902	Download
SwinIR-DR	11.9M	121.1	Set5	33.03	0.904	Download
DAT-DR	14.8M	155.1	Set5	33.17	0.906	Download

You can download the compressed non-blind SR models (remove 30% parameters) obtained by our Dual Regression Compression (DRC) approach for 4x SR.

Method	Params	FLOPs (G)	Dataset	PSNR (dB)	SSIM	Model Zoo
DRN-S30	3.1M	72.3	Set5	32.66	0.900	Download
SwinIR-light-DRC	635K	6.8	Set5	32.44	0.896	Download

You can download the compressed blind SR models (remove 30% parameters) obtained by our DRC approach for 4x SR.

Method	Params	FLOPs (G)	Dataset	PSNR (dB)	SSIM	Model Zoo
DCLS-DRC	14.2M	57.1	DIV2KRK	29.01	0.798	Download

Evaluating and Training

We put the detailed explanations about the code of evaluating and training in the corresponding folders. Please refer to more details in the README.md file within these folders.

Results

We achieved competitive performance. Detailed results can be found in the paper.

Click to expand

• Comparison results with SOTA SR methods for 4x SR in Table 1.

• Comparison results with lightweight SR methods for 4x SR in Table 2.

• Comparison results with blind SR methods for 4x SR in Table 3

• Visual comparison (x4) with SOTA methods for 4x SR.

• Visual comparison (x4) with compression methods for 4x SR.

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Citation

If you find this repository helpful, please consider citing:

@article{guo2022towards,
  title={Towards lightweight super-resolution with dual regression learning},
  author={Guo, Yong and Wang, Jingdong and Chen, Qi and Cao, Jiezhang and Deng, Zeshuai and Xu, Yanwu and Chen, Jian and Tan, Mingkui},
  journal={arXiv preprint arXiv:2207.07929},
  year={2022}
}