Sub Nyquist computational ghost imaging with deep learning.
We propose a deep learning computational ghost imaging (CGI) scheme to achieve sub Nyquist and high quality image reconstruction. Unlike the second order correlation CGI and compressive sensing CGI, which use lots of illumination patterns and a one dimensional (1 D) light intensity sequence (LIS) for image reconstruction, a deep neural network (DAttNet) is proposed to restore the target image only using the 1 D LIS. The DAttNet is trained with simulation data and retrieves the target image from experimental data. The experimental results indicate that the proposed scheme can provide high quality images with a sub Nyquist sampling ratio and performs better than the conventional and compressive sensing CGI methods in sub Nyquist sampling ratio conditions (e.g. 5.45% The proposed scheme has potential practical applications in underwater, real time and dynamic CGI.
Optics express