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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2020.3009999
|2 doi
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|a pubmed24n1044.xml
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|a eng
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| 100 |
1 |
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|a Fu, Ying
|e verfasserin
|4 aut
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| 245 |
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|a Joint Camera Spectral Response Selection and Hyperspectral Image Recovery
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 08.12.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Hyperspectral image (HSI) recovery from a single RGB image has attracted much attention, whose performance has recently been shown to be sensitive to the camera spectral response (CSR). In this paper, we present an efficient convolutional neural network (CNN) based method, which can jointly select the optimal CSR from a candidate dataset and learn a mapping to recover HSI from a single RGB image captured with this algorithmically selected camera under multi-chip or single-chip setups. Given a specific CSR, we first present a HSI recovery network, which accounts for the underlying characteristics of the HSI, including spectral nonlinear mapping and spatial similarity. Later, we append a CSR selection layer onto the recovery network, and the optimal CSR under both multi-chip and single-chip setups can thus be automatically determined from the network weights under the nonnegative sparse constraint. Experimental results on three hyperspectral datasets and two camera spectral response datasets demonstrate that our HSI recovery network outperforms state-of-the-art methods in terms of both quantitative metrics and perceptive quality, and the selection layer always returns a CSR consistent to the best one determined by exhaustive search. Finally, we show that our method can also perform well in the real capture system, and collect a hyperspectral flower dataset to evaluate the effect from HSI recovery on classification problem
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|a Journal Article
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| 700 |
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|a Zhang, Tao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zheng, Yinqiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Debing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Hua
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 1 vom: 15. Jan., Seite 256-272
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:44
|g year:2022
|g number:1
|g day:15
|g month:01
|g pages:256-272
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2020.3009999
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