Improving the Imaging Quality of Ghost Imaging Lidar via Sparsity Constraint by Time-Resolved Technique

Abstract

Ghost imaging via sparsity constraint (GISC)-which is developing into a new staring imaging lidar-can obtain both the range information and spatial distribution of a remote target with the use of the measurements below the Nyquist limit. In this work, schematics of both two-dimensional (2D) and three-dimensional (3D) GISC lidar are introduced. Compared with the 2D GISC lidar, we demonstrate by both simulation and experimentally that the signal-to-noise ratio of the 3D GISC lidar can be dramatically enhanced when a time-resolved technique is used to record the target's reflection signals and the orthogonal characteristic of the target's 3D surface structure is taken as a priori in the image reconstruction process. Some characteristics of the 2D and 3D GISC lidar systems are also discussed.

Performance analysis of ghost imaging lidar in background light environment

“…Therefore, by substituting Eqs. (12) and (13) into Eq. 3, and averaging over N measurements, we can obtain the image SNR for lidar system (B) as…”

“…Ghost imaging (GI) is a novel non-scanning imaging method to obtain a target's image with a single-pixel bucket detector [1][2][3][4][5][6]. Due to its capacity for high detection sensitivity, GI has aroused increasing interest in remote sensing, and a new imaging lidar system called GI lidar has gradually developed [7][8][9][10][11][12][13][14][15]. Up to now, there have been three types of three-dimensional GI lidars, namely narrow pulsed GI lidar, heterodyne GI lidar, and pulse-compression GI lidar via coherent detection [12][13][14][15].…”

“…Due to their distinct mechanisms, their advantages and disadvantages are obviously different. For narrow pulsed GI lidar, a series of high-power laser pulses with independent speckle configurations illuminate onto the target, and the backscattered intensity is directly received by a time-resolved bucket detector [7][8][9][10][11][12][13]. The structure of pulsed GI lidar is simple, but its imaging quality is subject to a low-detection signal-to-noise ratio (SNR).…”

Sub-Nyquist computational ghost imaging with deep learning

“…However, in 2008, Shapiro proposed a single-path GI which only has an object path, and he named it computational GI (CGI) [15]. Over the past few years, although GI has been applied in various fields, such as THz imaging [16], X-ray imaging [17], turbulence imaging [18], remote sensing [19] and multispectral imaging [20], the enormous signal acquisitions (measurements) prevent GI developing into a more actual technique.…”

Edge detection based on joint iteration ghost imaging

“…Then, many methods was proposed to improve the imaging quality, including compressive GI [10], differential GI [11], pseudo-inverse GI [12,13] and so on [14][15][16][17]. Especially, compressive GI can achieve the high-quality reconstruction image under undersampling [10], which promotes the practical application of GI technology [18][19][20][21][22][23][24][25][26].…”