A project in Statistical Image Processing. Extension of Deep unfolding network for image super-resolution for Blind Super Resolution via Kernel Estimation

In this project we aim to extend USRNet [1] for Blind Super Resolution. We added a step of blind kernel estimation [2] and noise STD estimation [3] algorithm. These additions show SOTA performance in Blind-SR

Clone the Repo:

git clone https://github.com/geopi1/Improved_USRNet.git

Download the Datasets (a bicubically downscaled version of DIV2K):

Download DIV2K
This link will download 2 folders:

• DIV2K_LR - the low resolution input
• DIV2K_HR_ds2 - Ground truth high resolution images

Tested and ran on:

• UBUNTU 18.04
• RTX 2080
• Nvidia driver 440.95.10
• cuda 10.1.243
• cudnn 7.6.5
• pytorch 1.5
• TF 1.14
  for additional package information please refer to requirements.txt or env.yml

1. Setup conda

   conda env create -f env.yml

   This will create a working environment named Blind_USRNet
2. Setup can also be performed with pip (virtual env) via the requirements.txt file

   python -m venv Blind_USRNet
   pip install -r requirements.txt

3. put images in right place

python main.py 

This will output 4 types of images to ./results:

• Default USRNet settings
• Default USRNet settings + estimated Noise STD
• Kernel estimated USRNet
• Kernel estimated USRNet + estimated Noise STD

Additionally, for each estimated image a side-by-side image with the LR version is saved with the degredation kernel (default or estimated)

To calculate the numeric results on the whole dataset run:

python utils/get_results.py

This will calculate the mean PSNR and SSIM on the SR<->HR on all the results

On the left, the LR image and on the Right the reconstructed HR image.
The blur kernel of the LR image is at the top left corner.
Default:

Ours:

Default:

Ours:

This project is licensed under the MIT License - see the LICENSE.md file for details

[1] Zhang, Kai, Luc Van Gool, and Radu Timofte. "Deep unfolding network for image super-resolution." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.

[2] Bell-Kligler, Sefi, Assaf Shocher, and Michal Irani. "Blind super-resolution kernel estimation using an internal-gan." Advances in Neural Information Processing Systems. 2019.

[3] Chen G , Zhu F , Heng P A . "An Efficient Statistical Method for Image Noise Level Estimation" 2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society, 2015.