Underwater compressive computational ghost imaging with wavelet enhancement.

ABSTRACT

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.