Gated viewing laser imaging with compressive sensing

Compressive Sensing Imaging Spectrometer for UV-Vis Stellar Spectroscopy: Instrumental Concept and Performance Analysis

Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the acquisition of the signal in a very compact way, merging acquisition and compression in a single step and, furthermore, offering the capability of using a limited number of detector elements to obtain a reconstructed image with a larger number of pixels. Although the CS paradigm has already been applied in several application domains, from medical diagnostics to microscopy, studies related to space applications are very limited. In this paper, we present and discuss the instrumental concept, optical design, and performances of a CS imaging spectrometer for ultraviolet-visible (UV-Vis) stellar spectroscopy. The instrument-which is pixel-limited in the entire 300 nm-650 nm spectral range-features spectral sampling that ranges from 2.2 nm@300 nm to 22 nm@650 nm, with a total of 50 samples for each spectrum. For data reconstruction quality, the results showed good performance, measured by several quality metrics chosen from those recommended by CCSDS. The designed instrument can achieve compression ratios of 20 or higher without a significant loss of information. A pros and cons analysis of the CS approach is finally carried out, highlighting main differences with respect to a traditional system.

Single pixel structured imaging through fog

We describe the application of structured imaging with a single-pixel camera to imaging through fog. We demonstrate the use of a high-pass filter on the detected bucket signals to suppress the effects of temporal variations of fog density and enable an effective reconstruction of the image. A quantitative analysis and comparison of several high-pass filters are demonstrated for the application. Both computational ghost imaging and compressive sensing techniques were used for image reconstruction and compressive sensing was observed to give a higher reconstructed image quality.

Compressive Sensing Based Three-Dimensional Imaging Method with Electro-Optic Modulation for Nonscanning Laser Radar

Low-cost Laser Detection and Ranging (LiDAR) is crucial to three-dimensional (3D) imaging in applications such as remote sensing, target detection, and machine vision. In conventional nonscanning time-of-flight (TOF) LiDAR, the intensity map is obtained by a detector array and the depth map is measured in the time domain which requires costly sensors and short laser pulses. To overcome such limitations, this paper presents a nonscanning 3D laser imaging method that combines compressive sensing (CS) techniques and electro-optic modulation. In this novel scheme, electro-optic modulation is applied to map the range information into the intensity of echo pulses symmetrically and the measurements of pattern projection with symmetrical structure are received by the low bandwidth detector. The 3D imaging can be extracted from two gain modulated images that are recovered by solving underdetermined inverse problems. An integrated regularization model is proposed for the recovery problems and the minimization functional model is solved by a proposed algorithm applying the alternating direction method of multiplier (ADMM) technique. The simulation results on various subrates for 3D imaging indicate that our proposed method is feasible and achieves performance improvement over conventional methods in systems with hardware limitations. This novel method will be highly valuable for practical applications with advantages of low cost and flexible structure at wavelengths beyond visible spectrum.

Imaging with SPADs and DMDs: Seeing through Diffraction-Photons

This paper addresses the problem of imaging in the presence of diffraction-photons. Diffraction-photons arise from the low contrast ratio of DMDs (∼1000:1), and very much degrade the quality of images captured by SPAD-based systems. Herein, a joint illumination-deconvolution scheme is designed to overcome diffraction-photons, enabling the acquisition of intensity and depth images. Additionally, a proof-of-concept experiment is conducted to demonstrate the viability of the designed scheme. It is shown that by co-designing the illumination and deconvolution phases of imaging, one can substantially overcome diffraction-photons.

Combining Non-Uniform Time Slice and Finite Difference to Improve 3D Ghost Imaging

Three-dimensional ghost imaging (3DGI) using a detector is widely used in many applications. The performance of 3DGI based on a uniform time slice is difficult to improve because obtaining an accurate time-slice position remains a challenge. This paper reports a novel structure based on non-uniform time slice combined with finite difference. In this approach, finite difference is beneficial to improving sensitivity of zero crossing to accurately obtain the position of the target in the field of view. Simultaneously, non-uniform time slice is used to quickly obtain 3DGI on an interesting target. Results show that better performances of 3DGI are obtained by our proposed method compared to the traditional method. Moreover, the relation between time slice and the signal-noise-ratio of 3DGI is discussed, and the optimal differential distance is obtained, thus motivating the development of a high-performance 3DGI.

Improved Range Estimation Model for Three-Dimensional (3D) Range Gated Reconstruction

Accuracy is an important measure of system performance and remains a challenge in 3D range gated reconstruction despite the advancement in laser and sensor technology. The weighted average model that is commonly used for range estimation is heavily influenced by the intensity variation due to various factors. Accuracy improvement in term of range estimation is therefore important to fully optimise the system performance. In this paper, a 3D range gated reconstruction model is derived based on the operating principles of range gated imaging and time slicing reconstruction, fundamental of radiant energy, Laser Detection And Ranging (LADAR), and Bidirectional Reflection Distribution Function (BRDF). Accordingly, a new range estimation model is proposed to alleviate the effects induced by distance, target reflection, and range distortion. From the experimental results, the proposed model outperforms the conventional weighted average model to improve the range estimation for better 3D reconstruction. The outcome demonstrated is of interest to various laser ranging applications and can be a reference for future works.

Range compensation for accurate 3D imaging system

Range determination has crucial dependency of intensity over distance due to the fact that output of a range gated system is the reflectivity and range information expressed in term of intensity. From our experimental study, the analyzed trend line of reflected intensity versus range agrees with the theoretical model where it underlies an inverse range-squared dependency. Considering the energy attenuation factor for a better three-dimensional (3D) solution, a range compensation model is derived based on time slicing technique to compensate the effect imposed by distance beyond an optimum range. A range gated imaging system with pulse profile feedback is proposed in order to apply the range compensation model. Experimental results show noticeable improvement as compared to the conventional weighted average method for reflected laser pulse data across different ranges and 3D surface reconstruction using the proposed range compensation model.

Three-dimensional imaging reconstruction algorithm of gated-viewing laser imaging with compressive sensing

Three-dimensional (3D) laser imaging combining compressive sensing (CS) has an advantage in lower power consumption and less imaging sensors; however, it brings enormous stress to subsequent calculation devices. In this paper we proposed a fast 3D imaging reconstruction algorithm to deal with time-slice images sampled by single-pixel detectors. The algorithm implements 3D imaging reconstruction before CS recovery, thus it saves plenty of runtime of CS recovery. Several experiments are conducted to verify the performance of the algorithm. Simulation results demonstrated that the proposed algorithm has better performance in terms of efficiency compared to an existing algorithm.

Photon counting compressive depth mapping

We demonstrate a compressed sensing, photon counting lidar system based on the single-pixel camera. Our technique recovers both depth and intensity maps from a single under-sampled set of incoherent, linear projections of a scene of interest at ultra-low light levels around 0.5 picowatts. Only two-dimensional reconstructions are required to image a three-dimensional scene. We demonstrate intensity imaging and depth mapping at 256 × 256 pixel transverse resolution with acquisition times as short as 3 seconds. We also show novelty filtering, reconstructing only the difference between two instances of a scene. Finally, we acquire 32 × 32 pixel real-time video for three-dimensional object tracking at 14 frames-per-second.