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This work focuses on the problem of fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image that admits high spatial and spectral resolutions. Existing algorithms are mostly based on joint low-rank factorization of the ma-tricized HSI and MSI. This framework is effective to some extent, but several challenges remain. First, it is unclear whether or not the super-resolution image is identifiable in theory under this framework, while identifiability usually plays an essential role in such estimation problems. Second, most algorithms assume that the degradation operators from the super-resolution image to the HSI and MSI are known or can be easily estimated - which is hardly true in practice. In this work, we propose a novel coupled tensor decomposition method that can effectively circumvent these issues. The proposed approach guarantees the identifiability of the super-resolution image under realistic conditions. The method can work even without knowing the spatial degradation operator, which could be hard to accurately estimate in practice. Simulations using AVIRIS Cuprite data are employed to demonstrate the effectiveness of the proposed approach.
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An Open Problem: Energy Data Super-Resolution
In this notes paper, we present an open problem to the Buildsys community: energy data super-resolution, referring to the task of estimating the power consumption of a home at a higher resolution given the low-resolution power consumption. Super-resolution is especially useful when the smart meters collect data at a very low-sampling rate owing to a plethora of issues such as bandwidth, pricing, old hardware, among others. The problem is motivated by the success of image super resolution in the computer vision community. In this paper, we formally introduce the problem and present baseline methods and the algorithms we used to "solve" this problem. We evaluate the performance of the algorithms on a real-world dataset and discuss the results. We also discuss what makes this problem hard and why a trivial baseline is hard to beat.
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- Award ID(s):
- 1646501
- PAR ID:
- 10211261
- Date Published:
- Journal Name:
- NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring
- Page Range / eLocation ID:
- 99 to 102
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3427771.3429995
