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Journal of Applied Remote Sensing

Efficient simultaneous image deconvolution and upsampling algorithm for low-resolution microwave sounder data
Author(s): Jing Qin; Igor Yanovsky; Wotao Yin
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Paper Abstract

Microwave imaging has been widely used in the prediction and tracking of hurricanes, typhoons, and tropical storms. Due to the limitations of sensors, the acquired remote sensing data are usually blurry and have relatively low resolution, which calls for the development of fast algorithms for deblurring and enhancing the resolution. We propose an efficient algorithm for simultaneous image deconvolution and upsampling for low-resolution microwave hurricane data. Our model involves convolution, downsampling, and the total variation regularization. After reformulating the model, we are able to apply the alternating direction method of multipliers and obtain three subproblems, each of which has a closed-form solution. We also extend the framework to the multichannel case with the multichannel total variation regularization. A variety of numerical experiments on synthetic and real Advanced Microwave Sounding Unit and Microwave Humidity Sounder data were conducted. The results demonstrate the outstanding performance of the proposed method.

Paper Details

Date Published: 24 December 2015
PDF: 15 pages
J. Appl. Remote Sens. 9(1) 095035 doi: 10.1117/1.JRS.9.095035
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Jing Qin, Univ. of California, Los Angeles (United States)
Igor Yanovsky, Univ. of California, Los Angeles (United States)
Jet Propulsion Lab. (United States)
Wotao Yin, Univ. of California, Los Angeles (United States)


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