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.
In the past decade, information theory has been studied extensively in computational imaging. In particular,
image matching by maximizing mutual information has been shown to yield good results in multimodal image
registration. However, there have been few rigorous studies to date that investigate the statistical aspect of
the resulting deformation fields. Different regularization techniques have been proposed, sometimes generating
deformations very different from one another. In this paper, we present a novel model for multimodal image
registration. The proposed method minimizes a purely information-theoretic functional consisting of mutual
information matching and unbiased regularization. The unbiased regularization term measures the magnitude of
deformations using either asymmetric Kullback-Leibler divergence or its symmetric version. The new multimodal
unbiased matching method, which allows for large topology preserving deformations, was tested using pairs of
two and three dimensional serial MRI images. We compared the results obtained using the proposed model to
those computed with a well-known mutual information based viscous fluid registration. A thorough statistical
analysis demonstrated the advantages of the proposed model over the multimodal fluid registration method when
recovering deformation fields and corresponding Jacobian maps.