Sub-Nyquist computational ghost imaging with orthonormalized colored noise pattern

TL;DR

This paper introduces an orthonormalization technique using colored noise patterns to enable sub-Nyquist computational ghost imaging, significantly reducing the number of patterns needed for high-quality image reconstruction.

Contribution

The study presents a novel orthonormalization approach with colored noise patterns for sub-Nyquist ghost imaging, improving efficiency over traditional methods.

Findings

High-quality images achieved with lower sampling ratios

Enhanced signal-to-noise ratio in noisy environments

Reduced number of patterns needed for image reconstruction

Abstract

Computational ghost imaging generally requires a large number of pattern illumination to obtain a high-quality image. The colored noise speckle pattern was recently proposed to substitute the white noise pattern in a variety of noisy environments and gave a significant signal-to-noise ratio enhancement even with a limited number of patterns. We propose and experimentally demonstrate here an orthonormalization approach based on the colored noise patterns to achieve sub-Nyquist computational ghost imaging. We tested the reconstructed image in quality indicators such as the contrast-to-noise ratio, the mean square error, the peak signal to noise ratio, and the correlation coefficient. The results suggest that our method can provide high-quality images while using a sampling ratio an order lower than the conventional methods.