A new super resolution method based on combined sparse representations for remote sensing imagery

Feng Li; LingLi Tang; ChranRong Li; Yi Guo; JunBin Gao

doi:10.1117/12.2029012

17 October 2013 A new super resolution method based on combined sparse representations for remote sensing imagery

Feng Li, LingLi Tang, ChranRong Li, Yi Guo, JunBin Gao

Proceedings Volume 8892, Image and Signal Processing for Remote Sensing XIX; 889204 (2013) https://doi.org/10.1117/12.2029012
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

While developing high resolution payloads, it is also necessary to make full use of the present spaceborne/airborne payload resources by super resolution (SR). SR is a technique of restoring a high spatial resolution image from a series of low resolution images of the same scene captured at different times in a short period. Common SR methods, however, may fail to overcome the irregular local warps and transformation in low resolution remote sensing images caused by platform vibration and air turbulence. It is also difficult to choose a generalized prior for remote sensing images for Maximum a Posteriori based SR methods. In this paper, irregular local warps and transformation within low resolution remote sensing images will be corrected by incorporating an elastic registration method. Moreover, combined sparse representation will be proposed for remote sensing SR problem. Experimental results show that the new method constructs a much better high resolution image than other common methods. This method is promising for real applications of restoring high resolution images from current low resolution on-orbit payloads.

Citation Download Citation

Feng Li, LingLi Tang, ChranRong Li, Yi Guo, and JunBin Gao "A new super resolution method based on combined sparse representations for remote sensing imagery", Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 889204 (17 October 2013); https://doi.org/10.1117/12.2029012

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