Efficient Simultaneous Image Deconvolution and Upsampling Algorithm for Low-Resolution Microwave Sounder Data

Jing Qin, Igor Yanovsky, Wotao Yin

Published in Journal of Applied Remote Sensing

Overview

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 (ADMM) 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 out- standing performance of the proposed method.

Citation

J. Qin, I. Yanovsky, and W. Yin, Efficient Simultaneous Image Deconvolution and Upsampling Algorithm for Low-Resolution Microwave Sounder Data, Journal of Applied Remote Sensing 9(1), 095035, 2015. DOI: 10.1117/1.JRS.9.095035


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