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

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!

您好，您在CLS的forward()方法中有一个循环16次的语句： for i in range(feature_pad.shape[1]): #对16个通道中的每个通道进行操作 feature_ch = feature_pad[:, i:i+1, :, :] clear_feature_ch = get_uperleft_denominator(feature_ch, kernel, kernel_P[:, i:i+1, :, :]) clear_features[:, i:i+1, :, :] = clear_feature_ch[:, :, ks:-ks, ks:-ks] 其中get_uperleft_denominator(feature_ch, kernel, kernel_P[:, i:i+1, :, :])方法我看了好多遍，完全看不懂，只知道这个方法是对经过填充的原始特征，预测的平滑滤波器，模糊核进行一些傅里叶变换操作。您能对下列的4~8行解释下这几行代码是在干嘛吗？跪跪谢！ ps（第四行方法里进行的循环移位操作看的怀疑人生，能看懂但是不知道为什么要这样做！哭）

1.# ------------------------------------------------------ 2.# -----------Constraint Least Square Filter------------- 3.def get_uperleft_denominator(img, kernel, grad_kernel): 4. ker_f = convert_psf2otf(kernel, img.size()) # discrete fourier transform of kernel 对模糊核进行离散傅里叶操作 5. ker_p = convert_psf2otf(grad_kernel, img.size()) # discrete fourier transform of kernel 6. denominator = inv_fft_kernel_est(ker_f, ker_p) 7. numerator = torch.rfft(img, 3, onesided=False) 8. deblur = deconv(denominator, numerator) 9. return deblur

您好，在您的README中“Dataset Preparation”节写到“To transform datasets to binary files for efficient IO, run:”，但是当我运行create_lmdb.py文件时，并不知道其中的img_folder应该指向什么样的训练数据路径。换句话说，在生成训练数据时，我应该将img_folder设置为DIV2K的HR图像，还是DIV2Kx4下的LR图像？

I am a new researcher in super-resolution work, for Blind Image Super-Resolution, I think we cannot get the SR image information and true kernel. But in your work, you have used this information, maybe this is not Blind Image Super-Resolution?

Hello,

MSU Graphics & Media Lab Video Group has recently launched two new Super-Resolution Benchmarks.

• Video Upscalers Benchmark: Quality Enhancement determines the best upscaling methods for increasing video resolution and improving visual quality.
• Super-Resolution for Video Compression benchmark aims to test Super-Resolution methods on compressed videos and select the best model for each video codec standard.

If you are interested in participating, you can add your algorithm following the submission steps:

• Submit for Video Upscalers Benchmark: Quality Enhancement
• Submit for Super-Resolution for Video Compression benchmark

We would be grateful for your feedback on our work!

您好，关于您论文中3.3节，有几个问题： 1、公式（10）是怎么得到的，需要最小化的不应该是||GiX↓s—Ri||2吗,就像Deep Wiener Decovolution中的公式（4a）。 2、为什么在特征空间中，平滑滤波器P和拉格朗日乘子可能不一致？如何理解？ 3、为什么利用神经网络也就是代码中的self.grad_filter可以预测出来一组具有隐式拉格朗日乘数的平滑滤波器，怎么体现的呢？什么原理？