High-turbidity underwater active single-pixel imaging based on generative adversarial networks with double Attention U-Net under low sampling rate

Single-pixel imaging (SPI) is widely used in underwater optical imaging because it has the feature of turbulence -free, little backscattering, and simple system structure. However, the image reconstruction quality of SPI is severely degraded as the increasing intensity of scattering medium in underwater environments. In this paper, an underwater active SPI system based on generative adversarial network with double Attention U-Net is proposed to reconstruct the underwater target image with high turbidity under low sampling rates. The generator of the proposed network use two modified Attention U-Net with double skip connections between different layers to improve the fidelity of reconstructed images. The atrous spatial pyramid pooling, and spatial squeeze and channel excitation modules are also integrated to obtain multi-scale information and remove redundant information. What is more, the least squares loss, content loss and mean structural similarity loss are also incorporated into the total loss function to stabilize the training process and avoid the gradient disappearance. Numerical simulations and physical experiments have shown that the proposed method can reconstruct underwater target images at a low sampling rate of 3.52% under the turbidity of 128NTU. The proposed method shows relatively strong reconstruction ability and generalization, and the PSNR and SSIM can be respectively improved by 0.75 times and 3 times compared with compressed sensing SPI. Our work provides a new insight into underwater SPI with high turbidity and can greatly improve the capability of underwater SPI.

Automated summary

In this paper, an underwater active SPI system based on generative adversarial network with double Attention U-Net is proposed to reconstruct the underwater target image with high turbidity under low sampling rates. The proposed method shows relatively strong reconstruction ability and generalization, and the PSNR and SSIM can be respectively improved by 0.75 times and 3 times compared with compressed sensing SPI.