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Journal of Spectroscopy
Volume 2018, Article ID 2698025, 7 pages
https://doi.org/10.1155/2018/2698025
Research Article

Hazardous Gas Emission Monitoring Based on High-Resolution Images

1Xi’an Institute of Applied Optics, Xi’an 710065, China
2School of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710071, China
3Chinese Flight Test Establishment, Xi’an 710089, China

Correspondence should be addressed to Xiaopeng Shao; nc.ude.naidix@oahspx

Received 28 September 2017; Accepted 21 November 2017; Published 4 March 2018

Academic Editor: Yufei Ma

Copyright © 2018 Wenjian Chen et al. 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.

Abstract

Air pollution presents unprecedentedly severe challenges to humans today. Various measures have been taken to monitor pollution from gas emissions and the changing atmosphere, of which imaging is of crucial importance. By images of target scenes, intuitional judgments and in-depth data are achievable. However, due to the limitations of imaging devices, effective and efficient monitoring work is often hindered by low-resolution target images. To deal with this problem, a superresolution reconstruction method was proposed in this study for high-resolution monitoring images. It was based on the idea of sparse representation. Particularly, multiple dictionary pairs were trained according to the gradient features of samples, and one optimal pair of dictionaries was chosen to reconstruct by judging the weighting of the information in different directions. Furthermore, the K-means singular value decomposition algorithm was used to train the dictionaries and the orthogonal matching pursuit algorithm was employed to calculate the sparse coding coefficients. Finally, the experiment’s results demonstrated its advantages in both visual fidelity and numerical measures.