# Randomness assisted in-line holography with deep learning

arXiv: https://arxiv.org/abs/2303.17660 · HTML: https://mlsanity.com/paper/2303.17660/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2023.

## Built on by (most cited first, 1 of 1)

- [Enhancing Digital Hologram Reconstruction Using Reverse-Attention Loss for Untrained Physics-Driven Deep Learning Models with Uncertain Distance](https://mlsanity.com/paper/2403.12056/) (2024)

## Abstract (arXiv)

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.