DeepDeblur-PyTorch

This repository contains the unofficial PyTorch re-implementation of the paper:
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
Seungjun Nah, Tae Hyun Kim, Kyoung Mu Lee
In CVPR 2017

arXiv | Original Torch code

Note: This code is for personal project. Details of the implementation may different from the original code. Should you be making use of this repository for your own research, please take it with care.

Performance

Performance on GOPRO test dataset (PSNR/SSIM)

K	Original paper	This repository
3	29.08dB / 0.9135	29.22dB / 0.8734*

* Original paper used MATLAB implementation whereas this repo used skimage implementation.

Usage

Installation

git clone https://github.com/jinsuyoo/DeepDeblur-PyTorch.git
cd DeepDeblur-PyTorch

Requirements

• Linux (tested on Ubuntu 16.04 LTS)
• Anaconda
• Python 3.7
• PyTorch 1.5.0
• TensorFlow (used for Tensorboard visualization)

and the Python dependencies listed in requirements.txt file.

To install, please run the following commands.

conda create -n DeepDeblur python=3.7
conda activate DeepDeblur
conda install pytorch==1.5.0 torchvision==0.6.0 cudatoolkit=10.2.89 -c pytorch
pip install -r requirements.txt

Prepare dataset

Download GOPRO dataset from here.

The data folder should be organized as,

/datasets/GOPRO_Large

Training model

python train.py

Test model

python test.py

Training logs

Total G loss	Content loss	Adversarial loss	D loss

PSNR on validation set (same as test set in this research)

Acknowledgments

Augmentation code borrows from the original code.