Super-Resolution Network for Multiple Degradations (SRMD)

PyTorch Implementation of Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR 2018)

All the files in the directory kernels are from [1].

Requirements

• python 3.6
• pytorch==0.4.0
• visdom
• pillow
• h5py

Usage

First, download images from the Internet and put them into a folder Database as follows:

+---SRMD-pytorch
    +---Database
        +---train                  # For training
        |       1.png
        |       10.jpg
        |        ...
        \---test                   # For testing
                5.png
                10.bmp
                 ...

Second, train the network

$ python main.py --scale_factor=2 --mode='train'

Third, do testing (Not implemented yet)

$ python main.py --scale_factor=2 --mode='test'

Results

• First column: LR image, Second column: SR image, Third column: HR image

Images (SRMDNFx2)

Images (SRMDNFx3)

Images (SRMDNFx4)

Notes

• The implementation is slightly different with one in original paper.
• I put a sigmoid function right after the last convolutional layer.
  • No sigmoid resulted in unstable results (bouncing pixels even after large iterations)

To-Do

• To implement method test() of class Solver considering users' favor (specific blur kernel as input)
• To train on the other scale factors (SRMDNFx3, SRMDNFx4)
• To consider additive noise after downsampling (SRMDx2, SRMDx3, SRMDx4)
• To evaluate quantitative performances of the trained networks

Reference

[1] MatConvNet implementation of one author of the paper (only test code available, as of June 27, 2018)