Awesome

MEDAEP

Multi-channels and Multi-models based Autoencoding Priors for Grayscale Image Restoration
The Code is created based on the method described in the following paper:
“Multi-channels and multi-models based autoencoding priors for grayscale image restoration”
Author: Li Sanqian, Liuqiegen, Qinbinjie, Wang. Yuhao, Liang. Dong.
Date : 12/2018
Version : 1.0
The code and the algorithm are for non-comercial use only.
Copyright 2018, Department of Electronic Information Engineering, Nanchang University.
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Medical AI research center, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

MEDAEP: Multi-channels and Multi-models based Autoencoding Priors for Grayscale Image Restoration

Illustration of EDAEP Network Scheme

Illustration of MEDAEP Network Scheme

Visual Quality Comparison of Image Deblurring on Gray Image “Boats”.

(Gaussian blur kernel size is 25, noisy level is 2.55) From Top to Bottom and from Left to Right: Ground-truth, blurred image,LevinSps, EPLL, IRCNN, DMSP, DPE, DAEP , EDAEP and MEDAEP.

[Paper]

@article{li2019multi,
title={Multi-Channel and Multi-Model-Based Autoencoding Prior for Grayscale Image Restoration},
author={Li, Sanqian and Qin, Binjie and Xiao, Jing and Liu, Qiegen and Wang, Yuhao and Liang, Dong},
journal={IEEE Transactions on Image Processing},
volume={29},
pages={142--156},
year={2019},
publisher={IEEE}
}

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