Super-Resolution Phase Retrieval Network for Single-Pattern Structured Light 3D Imaging

doi:10.1109/TIP.2022.3230245

2023-01-01

DOI：10.1109/TIP.2022.3230245

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摘要

En 中文

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 1x , 2x , and 4x super-resolution phase retrieval tasks.

Keyword:

Three-dimensional displays

Imaging

Superresolution

Periodic structures

Deep learning

Decoding

Feature extraction

Structured light

super-resolution

single-pattern

phase retrieval