Underwater compressive computational ghost imaging with wavelet enhancement

We propose a compressive Hadamard computational ghost imaging (CGI) method to restore clear images of objects in the underwater environment. We construct an underwater CGI system model and develop a total variation regularization prior-based compressed-sensing algorithm for the CGI image reconstruction. We design a wavelet enhancement algorithm to further denoise and enhance the quality of the CGI image. We build an experimental setup and implement a series of experiments. The effectiveness and advantages of the proposed method are experimentally investigated. The results show that the proposed method can achieve clear imaging for underwater objects with a sub-Nyquist sampling ratio. The proposed method is helpful for improving the image quality of the underwater CGI. (C) 2021 Optical Society of America.

Automated summary

In this study, a compressive Hadamard computational ghost imaging (CGI) method was proposed to restore clear images of objects in the underwater environment. Through experimental investigations, the proposed method was shown to achieve clear imaging for underwater objects with a sub-Nyquist sampling ratio, demonstrating its effectiveness and advantages in improving image quality for underwater CGI.