Official PyTorch implementation of the paper "Deep Constrained Least Squares for Blind Image Super-Resolution", CVPR 2022.

Overview

Deep Constrained Least Squares for Blind Image Super-Resolution

[Paper]

This is the official implementation of 'Deep Constrained Least Squares for Blind Image Super-Resolution', CVPR 2022.

Updates

[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.

DCLS

Overview

DCLS

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

  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:

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)

ISO kernel

Comparison on Anisotropic Gaussian kernels (DIV2KRK)

ANISO kernel

Citations

If our code helps your research or work, please consider citing our paper. The following is a BibTeX reference.

@article{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},
  journal={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

Contact

email: [[email protected]]

Acknowledgement

This project is based on [DAN], [MMSR] and [BasicSR].

Issues
  • issue about x2 SR training process

    issue about x2 SR training process

    image Hi, when I train the model under setting1_x2, I can not obtain a good result. Maybe I missed something in my training. Could you give some advice on it?

    #### general settings
    name: DCLSx2_setting1
    use_tb_logger: true
    model: blind
    distortion: sr
    scale: 2
    gpu_ids: [0, 1, 2, 3]
    pca_matrix_path: ../../../pca_matrix/DCLS/pca_matrix.pth
    
    degradation:
      random_kernel: True
      ksize: 21
      code_length: 10
      sig_min: 0.2
      sig_max: 2.0
      rate_iso: 1.0
      random_disturb: false
    
    #### datasets
    datasets:
      train:
        name: DIV2K
        mode: GT
        dataroot_GT: /datasets/DF2K/HR/x2HR.lmdb
    
        use_shuffle: true
        n_workers: 4  # per GPU
        batch_size: 64
        GT_size: 128
        LR_size: 64
        use_flip: true
        use_rot: true
        color: RGB
      val:
        name: Set5
        mode: LQGT
        dataroot_GT: /datasets/Set5/x2HR.lmdb
        dataroot_LQ: /datasets/Set5/x2LRblur.lmdb
    
    #### network structures
    network_G:
      which_model_G: DCLS
      setting:
        nf: 64
        nb: 10
        ng: 5
        input_para: 256
        kernel_size: 21
    
    #### path
    path:
      pretrain_model_G: ~
      strict_load: true
      resume_state: ~
    
    #### training settings: learning rate scheme, loss
    train:
      lr_G: !!float 4e-4
      lr_E: !!float 4e-4
      lr_scheme: MultiStepLR
      beta1: 0.9
      beta2: 0.99
      niter: 500000
      warmup_iter: -1  # no warm up
      lr_steps: [200000, 400000]
      lr_gamma: 0.5
      eta_min: !!float 1e-7
    
      pixel_criterion: l1
      pixel_weight: 1.0
    
      manual_seed: 0
      val_freq: !!float 100
    
    #### logger
    logger:
      print_freq: 20
      save_checkpoint_freq: !!float 1000
    
    

    This is the config settings. Thank you for your reply!

    opened by wwlCape 3
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