Computational ghost imaging using deep learning

TL;DR

This paper introduces a deep learning approach to enhance the image quality of computational ghost imaging by effectively reducing noise in reconstructed images.

Contribution

It presents a novel deep neural network method that learns to denoise CGI images, improving image quality beyond traditional reconstruction techniques.

Findings

Deep learning significantly reduces noise in CGI images.

The method improves image clarity and detail reconstruction.

Enhanced CGI image quality demonstrated on experimental data.

Abstract

Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two- or three- dimensional images with a single or a few bucket detectors, the quality of the reconstructed images is reduced by noise due to the reconstruction of images from random patterns. In this study, we improve the quality of CGI images using deep learning. A deep neural network is used to automatically learn the features of noise-contaminated CGI images. After training, the network is able to predict low-noise images from new noise-contaminated CGI images.