Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Optics Express

DOI: 10.1364/oe.543358

Authors: Jing Hu, Xudong Chen, Yujie Cui, Shuo Liu, Zhili Lin

Language: English

Topic: Random lasers and scattering media

Subfield: Acoustics and Ultrasonics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funders: Youth Innovation Foundation of Xiamen, National Natural Science Foundation of China

Awards: G7299758857, G8452487438