Joint image pansharpening and registration via structure tensor total variation regularization

Pansharpening has been extensively studied in recent years. However, one drawback of the known image fusion methods is that the fusion performance is degraded by the registration error. We develops a variational framework for joint pansharpening and registration with structure tensor total variation...

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Bibliographic Details
Published in:Aerospace systems (Online) 2022-06, Vol.5 (2), p.277-283
Main Authors: Yuan, Yu, Pan, Han, Cao, Shu-qing, Jing, Zhong-liang
Format: Article
Language:English
Subjects:
Aerospace Technology and Astronautics
Engineering
Original Paper
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
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Summary:Pansharpening has been extensively studied in recent years. However, one drawback of the known image fusion methods is that the fusion performance is degraded by the registration error. We develops a variational framework for joint pansharpening and registration with structure tensor total variation regularization method. The proposed framework can fully capture the target image’s first-order information around a local neighborhood and align image gradient domain. An efficient optimization method based on the scheme of fast iterative shrinkage-thresholding algorithm (FISTA) is proposed to solve the objective fusion model. This framework consists of two key steps: (i) pansharpening with structure tensor total variation regularization; (ii) image registration in image gradient domain. Extensive experiments demonstrate the effectiveness of our method compared with the existing state-of-the-art fusion models.
ISSN:2523-3947
2523-3955
DOI:10.1007/s42401-022-00138-w