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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2022.3230245
|2 doi
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|a pubmed24n1183.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Song, Jianwen
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Super-resolution phase retrieval network for single-pattern structured light 3D imaging
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 04.04.2023
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Structured light 3D imaging is often used for obtaining accurate 3D information via phase retrieval. Single-pattern structured light 3D imaging is much faster than multi-pattern versions. Current phase retrieval methods for single-pattern structured light 3D imaging are however not accurate enough. Besides, the projector resolution in a structured light 3D imaging system is expensive to improve due to hardware costs. To address the issues of low accuracy and low resolution of single-pattern structured light 3D imaging, this work proposes a super-resolution phase retrieval network (SRPRNet). Specifically, a phase-shifting module is proposed to extract multi-scale features with different phase shifts, and a refinement and super-resolution module is proposed to obtain refined and super-resolution phase components. After phase demodulation and unwrapping, high-resolution absolute phase is obtained. A sine shifting loss and a cosine shifting loss are also introduced to form the regularization term of the loss function. As far as can be ascertained, the proposed SRPRNet is the first network for super-resolution phase retrieval by using a single pattern, and it can also be used for standard-resolution phase retrieval. Experimental results on three datasets show that SRPRNet achieves state-of-the-art performance on 1×, 2×, and 4× super-resolution phase retrieval tasks
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|a Journal Article
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1 |
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|a Liu, Kai
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sowmya, Arcot
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sun, Changming
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2022) vom: 22. Dez.
|w (DE-627)NLM09821456X
|x 1941-0042
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| 773 |
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|g volume:PP
|g year:2022
|g day:22
|g month:12
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|u http://dx.doi.org/10.1109/TIP.2022.3230245
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