Self-evolving ghost imaging

Ghost imaging can capture 2D images with a point detector instead of an array sensor. It therefore offers a solution to the challenge of building area format sensors in wavebands where such sensors are difficult and expensive to produce and opens up new imaging modalities due to high-performance single-pixel detectors. Traditionally, ghost imaging retrieves the image of an object offline, by correlating measured light intensities and applied illuminating patterns. Here we present a feedback-based approach for online updating of the imaging result that can bypass post-processing, termed self-evolving ghost imaging (SEGI). We introduce a genetic algorithm to optimize the illumination patterns in real-time to match the objects shape according to the measured total light intensity. We theoretically and experimentally demonstrate this concept for static and dynamic imaging. This method opens new perspectives for real-time ghost imaging in applications such as remote sensing (e.g. machine vision, LiDAR systems in autonomous vehicles) and biological imaging

Self-evolving ghost imaging

Funding Australian Research Council (DE200100074, DP190101058); China Scholarship Council (201607950009, 201706020170); University of Technology Sydney. Acknowledgment We thank Prof. Fengli Gao from Jilin University for the helpful discussion about PGPeer reviewedPublisher PD

Dual-mode adaptive-SVD ghost imaging

In this paper, we present a dual-mode adaptive singular value decomposition ghost imaging (A-SVD GI), which can be easily switched between the modes of imaging and edge detection. It can adaptively localize the foreground pixels via a threshold selection method. Then only the foreground region is illuminated by the singular value decomposition (SVD) - based patterns, consequently retrieving high-quality images with fewer sampling ratios. By changing the selecting range of foreground pixels, the A-SVD GI can be switched to the mode of edge detection to directly reveal the edge of objects, without needing the original image. We investigate the performance of these two modes through both numerical simulations and experiments. We also develop a single-round scheme to halve measurement numbers in experiments, instead of separately illuminating positive and negative patterns in traditional methods. The binarized SVD patterns, generated by the spatial dithering method, are modulated by a digital micromirror device (DMD) to speed up the data acquisition. This dual-mode A-SVD GI can be applied in various applications, such as remote sensing or target recognition, and could be further extended for multi-modality functional imaging/detection

Temporal compressive edge imaging enabled by a lensless diffuser camera

Lensless imagers based on diffusers or encoding masks enable high-dimensional imaging from a single shot measurement and have been applied in various applications. However, to further extract image information such as edge detection, conventional post-processing filtering operations are needed after the reconstruction of the original object images in the diffuser imaging systems. Here, we present the concept of a temporal compressive edge detection method based on a lensless diffuser camera, which can directly recover a time sequence of edge images of a moving object from a single-shot measurement, without further post-processing steps. Our approach provides higher image quality during edge detection, compared with the conventional post-processing method. We demonstrate the effectiveness of this approach by both numerical simulation and experiments. The proof-of-concept approach can be further developed with other image post-process operations or versatile computer vision assignments toward task-oriented intelligent lensless imaging systems.Comment: 5 pages, 4 figure

Quantitative and dark field ghost imaging with ultraviolet light

Ultraviolet (UV) imaging enables a diverse array of applications, such as material composition analysis, biological fluorescence imaging, and detecting defects in semiconductor manufacturing. However, scientific-grade UV cameras with high quantum efficiency are expensive and include a complex thermoelectric cooling system. Here, we demonstrate a UV computational ghost imaging (UV-CGI) method to provide a cost-effective UV imaging and detection strategy. By applying spatial-temporal illumination patterns and using a 325 nm laser source, a single-pixel detector is enough to reconstruct the images of objects. To demonstrate its capability for quantitative detection, we use UV-CGI to distinguish four UV-sensitive sunscreen areas with different densities on a sample. Furthermore, we demonstrate dark field UV-CGI in both transmission and reflection schemes. By only collecting the scattered light from objects, we can detect the edges of pure phase objects and small scratches on a compact disc. Our results showcase a feasible low-cost solution for non-destructive UV imaging and detection. By combining it with other imaging techniques, such as hyperspectral imaging or time-resolved imaging, a compact and versatile UV computational imaging platform may be realized for future applications.Comment: 9 pages, 5 figure

Temporal compressive edge imaging enabled by a lensless diffuser camera

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Evaluation of Bletilla striata Polysaccharide Deproteinized System Based on Entropy Weighted TOPSIS Model

The entropy TOPSIS model was used to compare the effects of Sevage method， acetonitrile method and trichloroacetic acid (TCA) method for removaling crude Bletilla striata polysaccharide (BSP) protein, and to explore the rationality of entropy TOPSIS for BSP deproteinization system evaluation. Based on the comprehensive score of BSP retention rate and protein removal rate, the optimal treatment conditions were screened out. Nine evaluation indicators including monosaccharide components, oxidative radical scavenging ability (ORAC), and half scavenging concentration of DPPH radicals (IC50) were constructed. Supplemented by UV and FTIR, the entropy TOPSIS was used to evaluate the results of three BSP deproteinization programs. After comprehensive score, the best extraction times of sevage method was 1 time. At same time, the protein removal rate was 22.9%, and the polysaccharide retention rate was 99.11%. The optimal mass concentration of the TCA method was 10%, when the protein removal rate was 70.64%, and the polysaccharide retention rate was 70.03%. Compared with the ORAC values and IC50 of the three polysaccharide, it was found that the value of polysaccharide ORAC treated by the acetonitrile method was higher than that of the positive control group (P<0.05), and the polysaccharides treated by the Sevage method had the strongest antioxidant activity. The BSP deproteinization evaluation system was analyzed by the entropy TOPSIS model, and the sevage method deproteinization effect was the best and the expected result. The results showed that the entropy TOPSIS model could be used in the evaluation of BSP deproteinization system

Constructing Completely Independent Spanning Trees in Crossed Cubes

The Completely Independent Spanning Trees (CISTs) are a very useful construct in a computer network. It can find applications in many important network functions, especially in reliable broadcasting, i.e., guaranteeing broadcasting operation in the presence of faulty nodes. The question for the existence of two CISTs in an arbitrary network is an NP-hard problem. Therefore most research on CISTs to date has been concerning networks of specific structures. In this paper, we propose an algorithm to construct two CISTs in the crossed cube, a prominent, widely studied variant of the well-known hypercube. The construction algorithm will be presented, and its correctness proved. Based on that, the existence of two CISTs in a special Bijective Connection network based on crossed cube is also discussed