Search

The large-aperture static imaging spectrometer (LASIS) is an interference spectrometer with high device stability, high throughput, a wide spectral range, and a high spectral resolution. One frame image of the original data cube acquired by the LASIS shows the image superimposed with interference fringes, which is distinctly different from traditional hyperspectral images. For compression studies using this new type of data, a lossless compression scheme that combines a novel data rearrange method and the lossless multispectral and hyperspectral image compression standard CCSDS-123 is presented. In the rearrange approach, the LASIS data cube is rearranged such that the interference information overlapped on the image can be separated, and the results are then processed using the CCSDS-123 standard. Then, several experiments are conducted to investigate the performance of the rearrange method and examine the impact of different CCSDS-123 parameter settings for the LASIS. The experimental results indicate that the proposed scheme provides a 32.9% higher ratio than traditional rearrange methods. Moreover, an adequate parameter combination for this compression scheme for LASIS is presented, and it yields a 19.6% improvement over the default settings suggested by the standard.

High-speed optical reconstruction of 3D-scenes can be achieved using digital holography with binary digital micromirror devices (DMD) or a ferroelectric spatial light modulator (fSLM). There are many algorithms for binarizing digital holograms. The most common are methods based on global and local thresholding and error diffusion techniques. In addition, hologram binarization is used in optical encryption, data compression, beam shaping, 3D-displays, nanofabrication, materials characterization, etc. This paper proposes an adaptive binarization method based on a combination of local threshold processing, hologram division into blocks, and error diffusion procedure (the LDE method). The method is applied for binarization of optically recorded and computer-generated digital holograms of flat objects and three-dimensional scenes. The quality of reconstructed images was compared with different methods of error diffusion and thresholding. Image reconstruction quality was up to 22% higher by various metrics than that one for standard binarization methods. The optical hologram reconstruction using DMD confirms the results of the numerical simulations.

Monitoring of subgrade settlement in railway and highway helps to maintain traffic safety and reduce infrastructure losses. When an excess subgrade settlement is detected, maintenances of the unqualified subgrade will be processed in time, and early warning of constructions will be processed to prevent the occurrence of safety accident. In this article, a noncontact, real-time, and unattended system based on optical method is introduced to monitor the subgrade settlement remotely. Several high-power infrared LED targets are mounted on the subgrade and they will move as the subgrade subsides. The movements of the LED targets are detected by an optical detection device based on the principle of optical imaging. The displacement data is remotely transmitted to a server for analysis via GPRS. The remote monitoring system has been applied to the monitoring of subgrade settlement of Zhensheng highway in Guizhou province of China in 2014. This technology helped to detect the subgrade settlement that was caused by the construction of Beiyinpo railway tunnel. Four test points on road surface were monitored at a sampling rate every 20 minutes. The effectiveness of the system has been verified by the practical application.

A segmented primary mirror is very important for extra-large astronomical telescopes, in order to detect the phase error between segmented mirrors. Traditional iterative algorithms are hard to detect co−phasing aberrations in real time due to the long-time iterative process. Deep learning has shown large potential in wavefront sensing, and it gradually focuses on detecting piston error. However, the current methods based on deep learning are mainly applied to coarse phase sensing, and only consider the detection of piston error with no tip/tilt errors, which is inconsistent with reality. In this paper, by innovatively designing the form of pupil mask, and further updating the OTF in the frequency domain, we obtain a new decoupled independent feature image that can simultaneously detect the piston error and tilt/tilt error of all sub-mirrors, which is effectively decoupled, and eliminates the dependence of the data set on the imaging object. Then, the Bi−GRU network is used to recover phase error information with high accuracy from the feature image proposed in this paper. The network’s detection accuracy ability is verified under single wavelength and broadband spectrum in simulation. This paper demonstrates that co−phasing errors can be accurately decoupled and extracted by the new feature image we proposed and will contribute to the fine phasing accuracy and practicability of the extended scenes for the segmented telescopes.

Infrared sensors have been deployed in many video surveillance systems because of the insensibility of their imaging procedure to some extreme conditions (e.g. low illumination condition, dim environment). To reduce human labor in video monitoring and perform intelligent infrared video understanding, an important issue we need to consider is how to locate the object of interest in consecutive video frames accurately. Therefore, developing a robust object tracking algorithm for infrared videos is necessary. However, the infrared information may not be reliable (e.g. thermal crossover), and appearance modeling with only the infrared modality may not be able to achieve good results. To address these issues, with the wide deployment of RGB-infrared camera systems, this paper proposes an infrared tracking framework in which information from RGB-modality will be exploited to assist the infrared object tracking. Specifically, within the tracking framework, in order to deal with the contaminated features caused by large appearance variations, an online non-negative feature template learning model is designed. The non-negative constraint enables the model to capture the local part-based characteristic of the target appearance. To ensure more important modality contribute more in appearance representation, an adaptive modality importance weight learning scheme is also incorporated in the proposed feature learning model. To guarantee the model optimality, an iterative optimization algorithm is derived. The experimental results on various RGB-infrared videos show the effectiveness of the proposed method.

For a long time, data compression was primarily intended to counteract the bandwidth limitations faced by computing equipment when handling internal and external data transfer and storage. Improvements in network and storage technology have gradually reduced these bandwidth limitations. Unfortunately, data volumes have continued to increase at the same time because of augmented resolutions, dynamic ranges, and so on. As a consequence, communication and storage bandwidth have increased insufficiently to inhibit the need for efficient compression. At the same time, owing to the heterogeneity of communication channels and sensing, storage, and display devices, compression systems have become subject to new functionality requirements that have primarily emerged to adapt to these heterogeneous operating circumstances of quality, resolution and temporal scalability, intelligent image transfer scheduling, random access, error-resilient transmission, and so on. This chapter intends to introduce the basic concepts of image and video coding technology and to provide future-proof guidelines for the design of compression systems operating on visual data. The chapter begins by discussing the individual components of a coding system and ends by providing some insight into a number of widely employed coding technologies, but it does not intend to be comprehensive.