Hazardous Gas Emission Monitoring Based on High-Resolution Images

Wenjian Chen,Yuanyuan Duan,Xuan Li,Wei Gao,Xiaopeng Shao,Yi Wang,Shiwei Ma

doi:10.1155/2018/2698025

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.

Highlights

Today, humans are facing the most severe situation of air pollution due to industrial emission, fuel combustion, motor vehicle exhaust emission, and so on [1,2,3,4]
The sparse coding-based superresolution (SCSR) algorithm proposed by Yang et al and the scale-up method using sparse representation (SISR) method proposed by Zeyde et al are both effective learning-based SR reconstruction methods, which try to obtain a HR image by training a high-resolution dictionary and a corresponding low-resolution (LR) dictionary
(ii) Divide the primitive areas into four subsets according to the gradient features, which are used as the data source to train four subdictionary sets, each including a HR dictionary and a corresponding LR dictionary by the K-means singular value decomposition (K-SVD) algorithm

Summary

Introduction

Humans are facing the most severe situation of air pollution due to industrial emission, fuel combustion, motor vehicle exhaust emission, and so on [1,2,3,4]. In the study of improving image resolution, single-frame image SR reconstruction algorithms based on dictionary learning show their unique advantage in combining the prior information of images in this research area. The SCSR algorithm proposed by Yang et al and the SISR method proposed by Zeyde et al are both effective learning-based SR reconstruction methods, which try to obtain a HR image by training a high-resolution dictionary and a corresponding low-resolution (LR) dictionary. If we separate a whole LR image into many patches and choose the dictionary couple with the highest similarity according to the features in the LR patches in the process of reconstructing HR patches, the resolution of the reconstructed monitoring image could be improved by a large margin. Several groups of experiments are completed and the results show that the images reconstructed by the proposed method are excellent on both subjective vision perception and objective evaluation value

Principle of SR Image Reconstruction

Experiment and Analysis

Conclusion