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If you use this software in an academic article, please consider citing:
@article{xu2018deep,
title={Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration},
author={Xu, Wenjia and Xu, Guangluan and Wang, Yang and Sun, Xian and Lin, Daoyu and Wu, Yirong},
journal={Remote Sensing},
volume={10},
number={12},
pages={1893},
year={2018},
publisher={Multidisciplinary Digital Publishing Institute}
}
Method overview
We propose a novel method named deep memory connected network (DMCN) based on the convolutional neural network to achieve image restoration. We build local and global memory connections to combine image detail with global information. To further reduce parameters and ease time consumption, we propose Downsampling Units, shrinking the spatial size of feature maps. The network can achieve Gaussian image denoising and single image super-resolution (SR).
Requirements
Python 3
PyTorch
Python Packages:
matplotlib
cv2
h5py
numpy
skimage
tensorboardX
torchvision
About
The code for paper "Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration"