GCD Based Blind Super-Resolution for Remote Sensing Applications

Neerav Sharma, P. P. Dash, Priyanka Saxena
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Abstract

The importance of remote sensing imageries is growing day by day. Extraction of fine details of desired regions worth for further processing and decision making. Usually the data bases of remote sensing imageries are very huge that overburden the processor. Super-Resolution overcomes this problem and yields a high-quality output in less time consumption. This paper aims to give a brief idea about one of the approaches of super-resolution known as blind super-resolution reconstruction approach. In this approach, Greatest Common Divisor (GCD) algorithm is embedded into the blind reconstruction technique. The HR images obtained from this method is compared with the interpolated images. The results shows the efficacy of the proposed method. The paper tries to overcome the limitations of the super resolution approach and a conclusive discussion of the whole method has been discussed.
基于GCD的盲超分辨率遥感应用
遥感影像的重要性与日俱增。提取所需区域的精细细节,为进一步处理和决策提供价值。通常遥感图像的数据库非常庞大,使处理器负担过重。超分辨率克服了这个问题,在更短的时间内产生高质量的输出。本文简要介绍了一种超分辨方法——盲超分辨重建方法。该方法将最大公约数(GCD)算法嵌入到盲重建技术中。将该方法得到的HR图像与插值后的图像进行了比较。实验结果表明了该方法的有效性。本文试图克服超分辨率方法的局限性,并对整个方法进行了结论性的讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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