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