Skip to content

Deep Spectral-Spatial Network (DSSN) for Single Image Deblurring

License

Notifications You must be signed in to change notification settings

SeokjaeLIM/DSSN_release

Repository files navigation

DSSN_release (we reuploaded the codes after errors are corrected)

This repository is a Pytorch implementation of the paper "Deep Spectral-Spatial Network for Single Image Deblurring"

Seokjae Lim, Jin Kim and Wonjun Kim
IEEE Signal Processing Letters

When using this code in your research, please cite the following paper:

Seokjae Lim, Jin Kim and Wonjun Kim, "Deep Spectral-Spatial Network for Single Image Deblurring," IEEE Signal Processing Letters vol. 27, no. 1, pp. 835-839, May 2020.

@ARTICLE{9094296,
author={S. {Lim} and J. {Kim} and W. {Kim}},
journal={IEEE Signal Processing Letters}, 
title={Deep Spectral-Spatial Network for Single Image Deblurring}, 
year={2020},
volume={27},
number={1},
pages={835-839},}

Model architecture

examples

Experimental results with state-of-the art methods on the GOPRO dataset

examples Several results of single image deblurring. First row : input blurry images selected from the GOPRO dataset. Second row : deblurring results by Tao et al. Third row : deblurring result by Zhang et al. Fourth row : deblurring results by the proposed method. Note that blurred regions (red and yellow-colored rectangles) are enlarged for better view. Best views in colors.

Experimental results with state-of-the art methods on the Köhler dataset

examples Several results of single image deblurring. First column : input blurry images selected from the Köhler dataset. Second column : deblurring results by Nah et al. Third column : deblurring result by Zhang et al. Fourth column : deblurring results by the proposed method. Note that all experiments are conducted with parameters, which are trained based on the GOPRO dataset, without any modification.

Requirements

  • Python >= 3.5
  • Pytorch 0.4.0
  • Ubuntu 16.04
  • CUDA 8 (if CUDA available)
  • cuDNN (if CUDA available)

Pretrained models

You can download pretrained DSSN model

Test result

You can download test results of our DSSN Model

Note

  1. you should place the weights in the ./data/model/
  2. Dataset is also placed in the ./data directory (i.e., ./data/GoPro_Large)
  3. test results are saved in the ./data/result/

Training

  • Deep Spectral-Spatial network training
python main_ULT.py n

Testing

  • Deep Spectral-Spatial network testing
python main_ULT.py t

About

Deep Spectral-Spatial Network (DSSN) for Single Image Deblurring

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Languages