Self-evolving ghost imaging

TL;DR

This paper introduces self-evolving ghost imaging (SEGI), a real-time, feedback-based method that optimizes illumination patterns using a genetic algorithm, enabling online image reconstruction without post-processing for static and dynamic objects.

Contribution

It presents a novel feedback-based approach with a genetic algorithm for real-time, online ghost image reconstruction, bypassing traditional offline correlation methods.

Findings

Successfully demonstrated for static objects

Effective in dynamic imaging scenarios

Enables real-time ghost imaging applications

Abstract

Ghost imaging can capture 2D images with a point detector instead of an array sensor. It therefore offers a solution to the challenge of building area format sensors in wavebands where such sensors are difficult and expensive to produce and opens up new imaging modalities due to high-performance single-pixel detectors. Traditionally, ghost imaging retrieves the image of an object offline, by correlating measured light intensities and applied illuminating patterns. Here we present a feedback-based approach for online updating of the imaging result that can bypass post-processing, termed self-evolving ghost imaging (SEGI). We introduce a genetic algorithm to optimize the illumination patterns in real-time to match the objects shape according to the measured total light intensity. We theoretically and experimentally demonstrate this concept for static and dynamic imaging. This method opens new perspectives for real-time ghost imaging in applications such as remote sensing (e.g. machine vision, LiDAR systems in autonomous vehicles) and biological imaging.