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Sensors 2018, 18(8), 2587; https://doi.org/10.3390/s18082587

Infrared Image Super Resolution by Combining Compressive Sensing and Deep Learning

1
University of Chinese Academy of Sciences, Beijing 101408, China
2
Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Shanghai 200083, China
*
Author to whom correspondence should be addressed.
Received: 12 June 2018 / Revised: 26 July 2018 / Accepted: 3 August 2018 / Published: 7 August 2018
(This article belongs to the Section Remote Sensors)
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Abstract

Super resolution methods alleviate the high cost and high difficulty in applying high resolution infrared image sensors. In this paper we present a novel single image super resolution method for infrared images by combining compressive sensing theory and deep learning. Low resolution images can be regarded as the compressed sampling results of the high resolution ones in compressive sensing. With sparsity in this theory, higher resolution images can be reconstructed. However, because of diverse level of sparsity for different images, the output contains noise and loss of high frequency information. Deep convolutional neural network provides a solution to relieve the noise and supplement some missing high frequency information. By concatenating two methods, we manage to produce better results in super resolution tasks for infrared images than SRCNN and ScSR. PSNR and SSIM values are used to quantify the performance. Applying our method to open datasets and actual infrared imaging experiments, we also find better visual results are preserved. View Full-Text
Keywords: super resolution; infrared images; compressive sensing; deep learning; convolutional neural networks super resolution; infrared images; compressive sensing; deep learning; convolutional neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zhang, X.; Li, C.; Meng, Q.; Liu, S.; Zhang, Y.; Wang, J. Infrared Image Super Resolution by Combining Compressive Sensing and Deep Learning. Sensors 2018, 18, 2587.

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