kky7 / MSSNet

[ECCVW 2022] Official Pytorch Implementation for "MSSNet: Multi-Scale-Stage Network for Single Image Deblurring"

Multi-Scale-Stage Network for Single Image Deblurring

Project | Paper | Supple

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

Training of MSSNet

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

• Python
• Pytorch 1.4 or 1.7
• scikit-image
• Tensorboard

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

• train_datalist : Text file with the path of image for training

• val_datalist : Text file with the path of image for validation

• checkdir : Path to save checkpoints

• loadchdir : Path of checkpoint to load

• data_root_dir : Root path of data in train_datalist

• val_root_dir : Root path of data in val_datalist

• isloadch : Whether to load checkpoint

• isval : Whether to use validation

• mgpu : Whether to use multiple gpu

• wf, scale, vscale : Hyper-Parameters for channel size

  Model	wf	scale	vscale
  MSSNet_small	20	40	40
  MSSNet	54	42	42
  MSSNet_large	80	50	50

Testing

Run

sh sh_test_mssnet.sh

Arguments

• test_datalist : Text file with the path of image for testing
• data_root_dir : Root path of data in test_datalist
• load_dir : Path of checkpoint to load
• outdir : Path to save test results
• is_save : Whether to save test results
• is_eval : Whether to evaluate the model on the GoPro test dataset using psnr of skimage.metrics

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}
}