Efficient single-pixel imaging based on a compact fiber laser array and untrained neural network

Abstract

This paper presents an efficient scheme for single-pixel imaging (SPI) utilizing a phase-controlled fiber laser array and an untrained deep neural network. The fiber lasers are arranged in a compact hexagonal structure and coherently combined to generate illuminating light fields. Through the utilization of high-speed electro-optic modulators in each individual fiber laser module, the randomly modulated fiber laser array enables rapid speckle projection onto the object of interest. Furthermore, the untrained deep neural network is incorporated into the image reconstructing process to enhance the quality of the reconstructed images. Through simulations and experiments, we validate the feasibility of the proposed method and successfully achieve high-quality SPI utilizing the coherent fiber laser array at a sampling ratio of 1.6%. Given its potential for high emitting power and rapid modulation, the SPI scheme based on the fiber laser array holds promise for broad applications in remote sensing and other applicable fields. Graphical Abstract

Low-sampling high-quality Hadamard and Fourier single-pixel imaging through automated optimization neural network

“…In SPI, the object is illuminated by the modulated light fields generated from a variety of devices, including rotating ground glass plate [8,9], Digital Micromirror Devices (DMD) [6,10,11], liquid crystal spatial light modulator (LC-SLM) [12][13][14], LED-based array [15][16][17], multimode fiber (MMF) [18], Silicon-based optical phased array (OPA) [19], fiber laser array [20] and so on. And the transmitted or reflected light intensities from the object are measured by the SPD.…”