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Multi-Scale-Stage Network for Single Image Deblurring

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Official Implementation of ECCVW Paper

MSSNet: Multi-Scale-Stage Network for Single Image Deblurring
Kiyeon Kim, Seungyong Lee, Sunghyun Cho
POSTECH
ECCV 2022 Workshop (AIM 2022)

Architecture

Network Architecture

<img src="./img/mssnet.jpg" width="500">

Training of MSSNet

<img src="./img/training.jpg" width="500">

Installation

git clone https://github.com/kky7/MSSNet.git

To install warmup scheduler, refer MPRNet

cd pytorch-gradual-warmup-lr; python setup.py install; cd ..

Dependencies

Download

Dataset

Train [GOPRO_Large]

Test [Google Drive]

Pre-trained models

GOPRO [Google Drive]

RealBlur [Google Drive]

Training

if you use one gpu or multiple gpu using data parallel, run

sh sh_train_mssnet.sh

if you use multiple gpu using distributed data parallel, run

sh sh_train_mssnet_ddp.sh

Arguments

Testing

Run

sh sh_test_mssnet.sh

Arguments

Evaluation

Run evaluate_gopro.m file to evaluate model on the gopro dataset.
This code is based on the MPRNet.

Acknowledgment

The code is based on the MPRNet, MIMO-UNet and ddp_example.

Citation

@inproceedings{Kim2022MSSNet,
author = {Kim, Kiyeon and Lee, Seungyong and Cho, Sunghyun},
title = {MSSNet: Multi-Scale-Stage Network for Single Image Deblurring},
booktitle = {Proc. of ECCVW (AIM)},
year = {2022}
}