Single-pixel imaging based on deep learning

TL;DR

This paper reviews recent advances in single-pixel imaging enhanced by deep learning, highlighting improved image quality, faster reconstruction, and diverse applications like super-resolution and encryption.

Contribution

It provides a comprehensive analysis of deep learning algorithms and implementation techniques applied to single-pixel imaging across various fields.

Findings

Deep learning significantly improves image reconstruction quality.

Fast reconstruction speeds enable real-time applications.

Diverse applications include super-resolution, encryption, and imaging through scattering media.

Abstract

Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors. However, the limited image quality and lengthy computational times for iterative reconstruction still impede the practical application of single-pixel imaging. Recently, deep learning has been introduced into single-pixel imaging, which has attracted a lot of attention due to its exceptional reconstruction quality, fast reconstruction speed, and the potential to complete advanced sensing tasks without reconstructing images. Here, this advance is discussed and some opinions are offered. Firstly, based on the fundamental principles of single-pixel imaging and deep learning, the principles and algorithms of single-pixel imaging based on deep learning are described and analyzed. Subsequently, the implementation technologies of single-pixel imaging based on deep learning are reviewed. They are divided into super-resolution single-pixel imaging, single-pixel imaging through scattering media, photon-level single-pixel imaging, optical encryption based on single-pixel imaging, color single-pixel imaging, and image-free sensing according to diverse application fields. Finally, major challenges and corresponding feasible approaches are discussed, as well as more possible applications in the future.