No abstract

The natural compound eye has received much attention in recent years due to its remarkable properties, such as its large field of view (FOV), compact structure, and high sensitivity to moving objects. Many studies have been devoted to mimicking the imaging system of the natural compound eye. The paper gives a review of state-of-the-art artificial compound eye imaging systems. Firstly, we introduce the imaging principle of three types of natural compound eye. Then, we divide current artificial compound eye imaging systems into four categories according to the difference of structural composition. Readers can easily grasp methods to build an artificial compound eye imaging system from the perspective of structural composition. Moreover, we compare the imaging performance of state-of-the-art artificial compound eye imaging systems, which provides a reference for readers to design system parameters of an artificial compound eye imaging system. Next, we present the applications of the artificial compound eye imaging system including imaging with a large FOV, imaging with high resolution, object distance detection, medical imaging, egomotion estimation, and navigation. Finally, an outlook of the artificial compound eye imaging system is highlighted.

Currently, colloidal quantum dots (CQDs)-based photodetectors are widely investigated due to their low cost and easy integration with optoelectronic devices. The requirements for a high-performance photodetector are a low dark current and a high photocurrent. Normally, photodetectors with a low dark current also possess a low photocurrent, or photodetectors with reduced dark current possess a reduced photocurrent, resulting in low detectivity. In this paper, a solution to suppress dark current and maintain a high photocurrent, i.e., use of poly­(methyl methacrylate) doped with Au nanoparticles (NPs) (i.e., PMMA:Au) as an interlayer for enhanced-performance tandem photodetectors, is presented. Our experimental data showed that the dark current through the tandem photodetector ITO/PEDOT:PSS/PbS:CsSnBr3/ZnO/PMMA:Au/CuSeN/PbS:CsSnBr3/ZnO/Ag is suppressed significantly; meanwhile, a high photocurrent is maintained after a PMMA:Au interlayer has been inserted between two subdetectors. The inserted PMMA:Au interlayer acts as storage nodes for electrons, reducing the dark current through the device; meanwhile, the photocurrent can be enhanced under illumination. As a result, the specific detectivity of the tandem photodetector with 35 nm PMMA:Au interlayer was enhanced significantly from 5.01 × 1012 to 2.7 × 1015 Jones under 300 μW/cm2 532 nm illumination at a low voltage of −1 V as compared to the device without a PMMA:Au interlayer. Further, the physical mechanism of enhanced performance is discussed in detail.

the potential of random pattern based computational ghost imaging (cGi) for real-time applications has been offset by its long image reconstruction time and inefficient reconstruction of complex diverse scenes. to overcome these problems, we propose a fast image reconstruction framework for cGi, called "DeepGhost", using deep convolutional autoencoder network to achieve real-time imaging at very low sampling rates (10-20%). By transferring prior-knowledge from STL-10 dataset to physical-data driven network, the proposed framework can reconstruct complex unseen targets with high accuracy. The experimental results show that the proposed method outperforms existing deep learning and state-of-the-art compressed sensing methods used for ghost imaging under similar conditions. the proposed method employs deep architecture with fast computation, and tackles the shortcomings of existing schemes i.e., inappropriate architecture, training on limited data under controlled settings, and employing shallow network for fast computation. Computational ghost imaging 1 acquires spatial information about an unknown target by illuminating it with a series of random binary patterns generated by a spatial light modulator (SLM). For each projected pattern, the light intensity back-reflected from the target plane is recorded by an ordinary photodiode. By correlating intensity measurements with corresponding projected patterns, the target image is reconstructed. One downside of CGI is the requirement of a large number of measurements to produce a good-quality image, which increases its imaging time. Despite the emergence of basis scan schemes 2 , CGI (using random patterns) is still employed in many applications due to its simplicity, inherent encryption of patterns 3 , and ease of deployment 4. Therefore, it is important to improve the efficiency of CGI by integrating it with some optimization technique to avoid complex (hardware based) methods 5 that fail to reap the benefits of reduced cost and simplicity in ghost imaging (GI). Owing to its advantages of low cost, robustness against noise and scattering, and ability to operate over long spectral range, CGI is widely used in many applications 6-8. In order to make CGI practical, more specifically for real-time imaging, it is important to reduce its imaging time. The imaging time of CGI can be subcategorized as data acquisition time and image reconstruction time. The data acquisition time of CGI depends on the required number of measurements and mainly on the projection rate of SLM. Recent advances in SLM technology make it easy to reduce data acquisition time by employing commercially available high-resolution digital micromirror devices (DMDs) operating at ~ 20 kHz. The acquisition time can also be reduced by employing some simple yet novel solutions 9,10. Therefore, the image reconstruction time remains the main bottleneck towards achieving high speed imaging in CGI. This image reconstruction time can be reduced by employing an efficient image reconstruction framework. Recently, comp...