[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

3. University of Electronic Science and Technology of China

[](https://paperswithcode.com/sota/blind-super-resolution-on-div2krk-2x?p=deep-constrained-least-squares-for-blind) [](https://paperswithcode.com/sota/blind-super-resolution-on-div2krk-4x?p=deep-constrained-least-squares-for-blind)  #### This is the official implementation of 'Deep Constrained Least Squares for Blind Image Super-Resolution', CVPR 2022. [[Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_Deep_Constrained_Least_Squares_for_Blind_Image_Super-Resolution_CVPR_2022_paper.pdf)] ### Updates [**2022.04.22**] 🎉🎉🎉 Our work **BSRT** won the 1st place in NTIRE 2022 BurstSR Challenge [[Paper]](https://arxiv.org/abs/2204.08332)[[Code]](https://github.com/Algolzw/BSRT). [**2022.03.09**] We released the code and provided the **pretrained model weights** [[here]](https://drive.google.com/drive/folders/135xCCLWSylBaNxh6B3I_UnCeox8AkVzC?usp=sharing). [**2022.03.02**] Our paper has been accepted by CVPR 2022.  ## Overview  ## Presentation Video: [[Youtube](https://www.youtube.com/watch?v=emXK78ckY_4)], [[Bilibili](https://www.bilibili.com/video/BV1cv4y1A7QL/)] ## 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: ```bash 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: ```bash 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 1. The core algorithm is in ``codes/config/DCLS``. 2. Please modify `` codes/config/DCLS/options `` to set path, iterations, and other parameters... 3. To train the model(s) in the paper, run below commands. For single GPU: ```bash cd codes/config/DCLS python3 train.py -opt=options/setting1/train_setting1_x4.yml ``` For distributed training ```bash 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 ```bash 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: [ziwei.ro@gmail.com] ## Acknowledgement This project is based on [[DAN](https://github.com/greatlog/DAN)], [[MMSR](https://github.com/open-mmlab/mmediting)] and [[BasicSR](https://github.com/xinntao/BasicSR)].