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Multi-band Image Super-resolution

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If you have a question about this talk, please contact Prof. Ramji Venkataramanan.

Multi-band imaging, which consists of acquiring a same scene in several hundreds of contiguous spectral bands (a 3D data cube), has opened a new range of relevant applications, such as target detection [Manolakis and Shaw, 2002], classification [C.-I Chang, 2003] and spectral unmixing However, while multi-band sensors provide abundant spectral information, their spatial resolution is generally more limited. Thus, fusing the multi-band image with other highly resolved images of the same scene, such as multispectral (MS) or panchromatic (PAN) images is an interesting problem, also known as multi-resolution image fusion. From an application point of view, this problem is also important as motivated by recent national programs, e.g., the Japanese next-generation space-borne hyperspectral image suite (HISUI), which fuses co-registered MS and HS images acquired over the same scene under the same conditions

In this talk, a new multi-band image fusion algorithm to enhance the resolution of multi-band image has been proposed. By exploiting intrinsic properties of the blurring and down-sampling matrices, a much more efficient fusion method has been developed thanks to a closed-form solution for the Sylvester matrix equation associated with maximizing the likelihood. The main contribution of this fusion scheme is that it gets rid of any simulation-based or optimization-based algorithms which are quite time consuming. Coupled with the alternating direction method of multipliers and the block coordinate descent, the proposed algorithm can be easily generalized to incorporate different priors or hyper-priors for the fusion problem, allowing for Bayesian estimators. We have tested the proposed algorithm in both synthetic data and real data. Results show that the proposed algorithm compares competitively with existing algorithms with the advantage of reducing the computational complexity significantly.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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