  We propose and demonstrate a holographic imaging scheme exploiting random
illuminations for recording hologram and then applying numerical reconstruction
and twin removal. We use an in-line holographic geometry to record the hologram
in terms of the second-order correlation and apply the numerical approach to
reconstruct the recorded hologram. The twin image issue of the in-line
holographic scheme is resolved by an unsupervised deep learning(DL) based
method using an auto-encoder scheme. This strategy helps to reconstruct
high-quality quantitative images in comparison to the conventional holography
where the hologram is recorded in the intensity rather than the second-order
intensity correlation. Experimental results are presented for two objects, and
a comparison of the reconstruction quality is given between the conventional
inline holography and the one obtained with the proposed technique.
