Deep-learned speckle pattern and its application to ghost imaging

TL;DR

This paper introduces a deep learning-based method for designing speckle patterns that enhance ghost imaging quality, especially at low sampling ratios, with broad potential applications in various imaging fields.

Contribution

It proposes a novel Speckle-Net framework for optimized speckle pattern generation tailored to ghost imaging, surpassing existing methods in quality and efficiency.

Findings

Outperforms traditional ghost imaging techniques at low sampling ratios

Generates high-quality images with fewer measurements

Demonstrates potential for diverse imaging applications

Abstract

In this paper, we present a method for speckle pattern design using deep learning. The speckle patterns possess unique features after experiencing convolutions in Speckle-Net, our well-designed framework for speckle pattern generation. We then apply our method to the computational ghost imaging system. The standard deep learning-assisted ghost imaging methods use the network to recognize the reconstructed objects or imaging algorithms. In contrast, this innovative application optimizes the illuminating speckle patterns via Speckle-Net with specific sampling ratios. Our method, therefore, outperforms the other techniques for ghost imaging, particularly its ability to retrieve high-quality images with extremely low sampling ratios. It opens a new route towards nontrivial speckle generation by referring to a standard loss function on specified objectives with the modified deep neural network. It also has great potential for applications in the fields of dynamic speckle illumination microscopy, structured illumination microscopy, x-ray imaging, photo-acoustic imaging, and optical lattices.