SE-FSCNet: full-scale connection network for single-shot phase demodulation

The accuracy of phase demodulation has significant impact on the accuracy of fringe projection 3D measurement. Currently, researches based on deep learning methods for extracting wrapped phase mostly use U-Net as the subject of network. The connection method between its hierarchies has certain short...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Optics express Ročník 32; číslo 9; s. 15295
Hlavní autoři: Song, Zeyu, Xue, Junpeng, Lu, Wenbo, Jia, Ran, Xu, Zhichao, Yu, Changzhi
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 22.04.2024
ISSN:1094-4087, 1094-4087
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The accuracy of phase demodulation has significant impact on the accuracy of fringe projection 3D measurement. Currently, researches based on deep learning methods for extracting wrapped phase mostly use U-Net as the subject of network. The connection method between its hierarchies has certain shortcomings in global information transmission, which hinders the improvement of wrapped phase prediction accuracy. We propose a single-shot phase demodulation method for fringe projection based on a novel full-scale connection network SE-FSCNet. The encoder and decoder of the SE-FSCNet have the same number of hierarchies but are not completely symmetrical. At the decoder a full-scale connection method and feature fusion module are designed so that SE-FSCNet has better abilities of feature transmission and utilization compared with U-Net. A channel attention module based on squeeze and excitation is also introduced to assign appropriate weights to features with different scales, which has been proved by the ablation study. The experiments conducted on the test set have demonstrated that the SE-FSCNet can achieve higher precision than the traditional Fourier transform method and the U-Net in phase demodulation.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1094-4087
1094-4087
DOI:10.1364/OE.520818