[CVPR2022] Deep Constrained Least Squares for Blind Image Super-Resolution
Ziwei Luo1, Haibin Huang2, Lei Yu1, Youwei Li1, Haoqiang Fan1, Shuaicheng Liu3
1. Megvii Research, 2. Kuaishou Technology
Paper]
This is the official implementation of 'Deep Constrained Least Squares for Blind Image Super-Resolution', CVPR 2022. [Updates
[2022.04.22]
[2022.03.09] We released the code and provided the pretrained model weights [here].
[2022.03.02] Our paper has been accepted by CVPR 2022.
Overview
Presentation Video:
Dependenices
- OS: Ubuntu 18.04
- nvidia :
- cuda: 10.1
- cudnn: 7.6.1
- python3
- pytorch >= 1.6
- Python packages: numpy opencv-python lmdb pyyaml
Dataset Preparation
We use DIV2K and Flickr2K as our training datasets (totally 3450 images).
To transform datasets to binary files for efficient IO, run:
python3 codes/scripts/create_lmdb.py
For evaluation of Isotropic Gaussian kernels (Gaussian8), we use five datasets, i.e., Set5, Set14, Urban100, BSD100 and Manga109.
To generate LRblur/LR/HR/Bicubic datasets paths, run:
python3 codes/scripts/generate_mod_blur_LR_bic.py
For evaluation of Anisotropic Gaussian kernels, we use DIV2KRK.
(You need to modify the file paths by yourself.)
Train
- The core algorithm is in
codes/config/DCLS
. - Please modify
codes/config/DCLS/options
to set path, iterations, and other parameters... - To train the model(s) in the paper, run below commands.
For single GPU:
cd codes/config/DCLS
python3 train.py -opt=options/setting1/train_setting1_x4.yml
For distributed training
cd codes/config/DCLS
python3 -m torch.distributed.launch --nproc_per_node=4 --master_poer=4321 train.py -opt=options/setting1/train_setting1_x4.yml --launcher pytorch
Or choose training options use
cd codes/config/DCLS
sh demo.sh
Evaluation
To evalute our method, please modify the benchmark path and model path and run
cd codes/config/DCLS
python3 test.py -opt=options/setting1/test_setting1_x4.yml
Results
Comparison on Isotropic Gaussian kernels (Gaussian8)
Comparison on Anisotropic Gaussian kernels (DIV2KRK)
Citations
If our code helps your research or work, please consider citing our paper. The following is a BibTeX reference.
@inproceedings{luo2022deep,
title={Deep constrained least squares for blind image super-resolution},
author={Luo, Ziwei and Huang, Haibin and Yu, Lei and Li, Youwei and Fan, Haoqiang and Liu, Shuaicheng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={17642--17652},
year={2022}
}
Contact
email: [[email protected]]