Factorized Multiscale Multiresolution Residual Network

Shivakanth Sujit & Deivalakshmi S, Seok-Bum Ko, "Factorized multi-scale multi-resolution residual network for single image deraining", Applied Intelligence (2021)

Overview

This repository provides a PyTorch implementation of FMMRNet.

If you find this code useful, please reference in your paper:

@article{sujit2021fmmrnet,
  title={Factorized multi-scale multi-resolution residual network for
single image deraining},
  author={Sujit, Shivakanth and S, Deivalakshmi and Ko, Seok-Bum},
  doi = {10.1007/s10489-021-02772-x},
  journal={Applied Intelligence},
  year={2021}
}

Installation

Clone the repository

git clone https://github.com/shivakanthsujit/FMMRNet.git
cd FMMRNet

Install dependencies

conda create -n env python=3.6
conda activate env
pip install -r requirements.txt

Download the datasets

Download the dataset from https://www.kaggle.com/shivakanthsujit/jrdr-deraining-dataset and place the JRDR folder in data.

Setup the Kaggle-API for downloading from the command line

pip install kaggle --upgrade
mkdir ~/.kaggle
cp kaggle.json ~/.kaggle/
chmod 600 ~/.kaggle/kaggle.json
kaggle datasets -h # Tests if the command works

Training

python train.py
python eval.py

Use Tensorboard to monitor the training.

tensorboard --logdir logs