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pro vyhledávání: '"Chabert, Marie"'
Regularization of inverse problems is of paramount importance in computational imaging. The ability of neural networks to learn efficient image representations has been recently exploited to design powerful data-driven regularizers. While state-of-th
Externí odkaz:
http://arxiv.org/abs/2311.17744
Autor:
Wang, Jin-Ju, Dobigeon, Nicolas, Chabert, Marie, Wang, Ding-Cheng, Huang, Ting-Zhu, Huang, Jie
In the context of Earth observation, the detection of changes is performed from multitemporal images acquired by sensors with possibly different spatial and/or spectral resolutions or even different modalities (e.g. optical, radar). Even limiting to
Externí odkaz:
http://arxiv.org/abs/2203.00948
Autor:
Wang, Jin-Ju, Dobigeon, Nicolas, Chabert, Marie, Wang, Ding-Cheng, Huang, Ting-Zhu, Huang, Jie
Publikováno v:
In Information Fusion July 2024 107
Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through sensors of di
Externí odkaz:
http://arxiv.org/abs/1807.08118
Unsupervised change detection techniques are generally constrained to two multi-band optical images acquired at different times through sensors sharing the same spatial and spectral resolution. This scenario is suitable for a straight comparison of h
Externí odkaz:
http://arxiv.org/abs/1804.03068
Change detection is one of the most challenging issues when analyzing remotely sensed images. Comparing several multi-date images acquired through the same kind of sensor is the most common scenario. Conversely, designing robust, flexible and scalabl
Externí odkaz:
http://arxiv.org/abs/1609.06074
Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through different kin
Externí odkaz:
http://arxiv.org/abs/1609.06076
Publikováno v:
In Information Fusion December 2020 64:293-317
Akademický článek
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Publikováno v:
In Computer Vision and Image Understanding December 2019 189