Ghost Image Processing

TL;DR

This paper explores how using different patterns for illumination and reconstruction in ghost imaging can enable direct measurement of processed images, such as edge detection, reducing noise amplification and simplifying image processing.

Contribution

It introduces a method to perform spatial filtering directly during ghost image reconstruction by choosing appropriate illumination patterns.

Findings

Reconstruction can act as a spatial filter like edge detection.

Direct measurement of processed images is possible, reducing post-processing noise.

The approach simplifies ghost imaging by integrating filtering into the reconstruction process.

Abstract

In computational ghost imaging the object is illuminated with a sequence of known patterns, and the scattered light is collected using a detector that has no spatial resolution. Using those patterns and the total intensity measurement from the detector, one can reconstruct the desired image. Here we study how the reconstructed image is modified if the patterns used for the reconstruction are not the same as the illumination patterns, and show that one can choose how to illuminate the object, such that the reconstruction process behaves like a spatial filtering operation on the image. The ability to measure directly a processed image, allows one to bypass the post-processing steps, and thus avoid any noise amplification they imply. As a simple example we show the case of an edge-detection filter.