EBRN is a recursive restoration model for single-image super-resolution This repository is a try to implement the EBRN model through Tensorflow 2.x and Keras API. As mentioned in the paper, the model was originally implemented on Pytorch, however their original code is not publicly available.
The EBRN was reported in the original paper here (https://ieeexplore.ieee.org/abstract/document/9010860). Some review articles mentioned its power in single-image super-resolution.
The jupyter file of Implementation_v02.1.ipynb is the start point. To start training, div2k images need to be dwonloded, unzipped and stored in /.div2k/images folder. Currently the scale of 4 (X4) is activated. Thus the following folders need to be present:
/DIV2K_train_HR
/DIV2K_valid_HR
/DIV2K_train_LR_bicubic/X4
/DIV2K_valid_LR_bicubic/X4
You can download DIV2K training and validation images for scale 4 and downgrade method of bicubic.
As it is tested, the program works with python 3.6-3.8 and tensorflow 2.0-2.2. You can install Anaconda and create a new environment with
conda env create -f env.yml
then activate it with
conda activate ebrn
The following figure demonstrates the block residual module (BRM) and the embedded block residual network (EBRN) as described in here:
The EBRN model with 4 BRM units created via graphviz and pydot: 
If this immplementation works for you, please cite it in your publications.
@misc{lajevardipour2020ebrn,
title={EBRN},
author={Alireza Lajevardipour},
year={2020},
howpublished={\url{https://github.com/alilajevardi/Embedded-Block-Residual-Network}},
}See LICENSE for details.