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- W4313541731 abstract "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." @default.
- W4313541731 created "2023-01-06" @default.
- W4313541731 creator A5034670275 @default.
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- W4313541731 date "2023-01-01" @default.
- W4313541731 modified "2023-10-09" @default.
- W4313541731 title "Super-Resolution Phase Retrieval Network for Single-Pattern Structured Light 3D Imaging" @default.
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- W4313541731 doi "https://doi.org/10.1109/tip.2022.3230245" @default.
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