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Open AccessArticle

Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration

1
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 1893; https://doi.org/10.3390/rs10121893
Received: 17 October 2018 / Revised: 15 November 2018 / Accepted: 21 November 2018 / Published: 27 November 2018
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
The spatial resolution and clarity of remote sensing images are crucial for many applications such as target detection and image classification. In the last several decades, tremendous image restoration tasks have shown great success in ordinary images. However, since remote sensing images are more complex and more blurry than ordinary images, most of the existing methods are not good enough for remote sensing image restoration. To address such problem, we propose a novel method named deep memory connected network (DMCN) based on the convolutional neural network to reconstruct high-quality images. 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. We verify its capability on two representative applications, Gaussian image denoising and single image super-resolution (SR). DMCN is tested on three remote sensing datasets with various spatial resolution. Experimental results indicate that our method yields promising improvements and better visual performance over the current state-of-the-art. The PSNR and SSIM improvements over the second best method are up to 0.3 dB. View Full-Text
Keywords: deep memory connected network; remote sensing; image restoration; single image super-resolution; image denoising deep memory connected network; remote sensing; image restoration; single image super-resolution; image denoising
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MDPI and ACS Style

Xu, W.; Xu, G.; Wang, Y.; Sun, X.; Lin, D.; Wu, Y. Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration. Remote Sens. 2018, 10, 1893.

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