Efficient simultaneous image deconvolution and upsampling algorithm for low-resolution microwave sounder data

Jing Qin; Igor Yanovsky; Wotao Yin

doi:10.1117/1.JRS.9.095035

24 December 2015 Efficient simultaneous image deconvolution and upsampling algorithm for low-resolution microwave sounder data

Jing Qin, Igor Yanovsky, Wotao Yin

Author Affiliations +

Funded by: NSF

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 095035 (December 2015). https://doi.org/10.1117/1.JRS.9.095035

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.

Citation Download Citation

Jing Qin, Igor Yanovsky, and Wotao Yin "Efficient simultaneous image deconvolution and upsampling algorithm for low-resolution microwave sounder data," Journal of Applied Remote Sensing 9(1), 095035 (24 December 2015). https://doi.org/10.1117/1.JRS.9.095035

Published: 24 December 2015

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