OAM-basis underwater single-pixel imaging based on deep learning at a low sampling rate

被引：0

作者：

Hu, Jing ^{[1
]}

Chen, Xudong ^{[1
]}

Cui, Yujie ^{[1
]}

Liu, Shuo ^{[1
]}

Lin, Zhili ^{[1
]}

机构：

[1] Huaqiao Univ, Coll Informat Sci & Engn, Fujian Key Lab Light Propagat & Transformat, Xiamen 361021, Fujian, Peoples R China

来源：

OPTICS EXPRESS | 2024年 / 32卷 / 27期

基金：

中国国家自然科学基金;

关键词：

Image sampling - Pixels - Underwater imaging - Underwater photography;

D O I：

10.1364/OE.543358

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Our study introduces a pioneering underwater single-pixel imaging approach that employs an orbital angular momentum (OAM) basis as a sampling scheme and a dual-attention residual U-Net generative adversarial network (DARU-GAN) as reconstruction algorithm. This method is designed to address the challenges of low sampling rates and high turbidity typically encountered in underwater environments. The integration of the OAM-basis sampling scheme and the improved reconstruction network not only enhances reconstruction quality but also ensures robust generalization capabilities, effectively restoring underwater target images even under the stringent conditions of a 3.125% sampling rate and 128 NTU turbidity. The integration of OAM beams' inherent turbulence resistance with DARU-GAN's advanced image reconstruction capabilities makes it an ideal solution for high-turbid underwater imaging applications.

引用

页码：49006 / 49020

页数：15