0.8% Nyquist computational ghost imaging via non-experimental deep learning

TL;DR

This paper introduces a deep learning-based computational ghost imaging framework that achieves high-quality image reconstruction at a sub-Nyquist sampling rate of 0.8%, demonstrating robustness to noise and generalization beyond training data.

Contribution

It presents a novel deep learning approach trained solely on simulations to enable efficient, low-sampling-rate ghost imaging with enhanced image quality and noise resilience.

Findings

Achieves high-quality images at 0.8% sampling rate

Outperforms traditional methods with white and pink noise

Robust to noisy environments and generalizes beyond training data

Abstract

We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns. The deep neural network in this work, which can learn the sensing model and enhance image reconstruction quality, is trained merely by simulation. To demonstrate the sub-Nyquist level in our work, the conventional computational ghost imaging results, reconstructed imaging results using white noise and pink noise via deep learning are compared under multiple sampling rates at different noise conditions. We show that the proposed scheme can provide high-quality images with a sampling rate of 0.8% even when the object is outside the training dataset, and it is robust to noisy environments. This method is excellent for various applications, particularly those that require a low sampling rate, fast reconstruction efficiency, or experience strong noise interference.