Compressed Imaging With a Separable Sensing Operator

Coded aperture compressive imaging array applied for surveillance systems

The increasing amount of data generated by space applications poses several challenges due to limited resources available onboard: power, memory, computation, data rate. In this paper, we propose Compressed Sensing (CS) as the key tool to face those challenges via compressive imaging. This signal processing technique, only recently applied to space applications, dramatically simplifies the image acquisition featuring native compression/encryption and enabling onboard image analysis, allowing to design simpler and lighter optical systems. In this paper, we try to answer the following question: To what extent are the potential benefits of CS going to materialize in a realistic “space big data” application scenario? To this purpose, we first review compressive imaging techniques and already existing prototypes and concepts, critically discussing the technological issues involved. Then, we propose a set of instrument concepts in the application domains of space science, planetary exploration and earth observation, most suitable for a CS–based application. For the most promising of them, we go deeper into the analysis showing preliminary reconstruction performance tests.

The broadband spectrum contains significantly more information than what the human eye can detect, with different wavelengths providing unique information about the intrinsic properties of an object. Recently, compressive sensing-based strategies for multi-spectral imaging via wavelength filtering at the pixel level on the imaging detector have been proposed for simultaneous acquisition of multi-spectral imaging data greatly reducing the acquisition times. To utilize such compressive sensing strategies for multi-spectral imaging, strong reconstruction algorithms that can reconstruct dense multi-spectral image cubes from the sparse compressively sensed observations are required. This paper proposes a comprehensive inter-spectral multi-layered conditional random field (IS-MCRF) sparse reconstruction framework for multi-spectral compressively sensed data captured using such acquisition strategies. The IS-MCRF framework leverages the information between neighboring spectral bands to better utilize the available information for reconstruction. The proposed framework was evaluated using compressively sensed multi-spectral acquisitions ranging from visible to near infrared spectral bands obtained by a simulated compressive sensing-based multi-spectral imaging system. Results show noticeable improvement over the existing sparse reconstruction techniques for compressive sensing-based multi-spectral imaging systems in preserving spatial and spectral fidelity.

Compressed sensing or compressive sampling (CS) is a new framework for simultaneous data sampling and compression which was proposed by Candes, Donoho, and Tao several years ago. Ever since the advent of a single-pixel camera, one of the CS applications - compressive imaging (CI, also referred as feature-specific imaging) has aroused more interest of numerous researchers. However, it is still a challenging problem to choose a simple and efficient measurement matrix in such a hardware system, especially for large scale image. In this paper, we propose a new measurement matrix whose rows are the odd rows of N order Hadamard matrix and discuss the validity of the matrix theoretically. The advantage of the matrix is its universality and easy implementation in the optical domain owing to its integer-valued elements. In addition, we demonstrate the validity of the matrix through the reconstruction of natural images using Orthogonal Matching Pursuit (OMP) algorithm. Due to the limitation of the memory of the hardware system and personal computer which is used to simulate the process, it is impossible to create such a large matrix that is used to conduct large scale images. In order to solve the problem, the block-wise notion is introduced to conduct large scale images and the experiments results present the validity of this method.

Multi-layered (or three-dimensional) optical memories have increasingly become a field of interest of interest in the development of a high density optical data storage devices. Systems that utilise multiple layer recording can achieve recording densities from 100 to 10,000 times higher than conventional optical data storage devices.

In its most general definition, optical data storage is simply using light as a tool to store and retrieve data. Optical data storage is found in popular consumer products. Compact Discs (CDs), Digital Versatile Discs (DVDs), and MiniDiscs (MDs), are all forms of optical data storage. More advanced forms of optical data storage include high‐speed devices and library products. All optical data storage devices use optical principles to achieve high data density, rugged packaging, reliable information retrieval, and cost‐effective production. There are many forms of optical storage media and many types of optical systems used to scan data. This chapter discusses the basic principles of optical data storage, types of commercial optical media available in 2003, several performance parameters, and some interesting prospects for future systems.

Thermal engineering of electron-trapping materials for “Smart-Write-In” optical data storage

Compressed sensing (CS) has emerged as a promising technique to jointly sense and compress sparse signals. One of the most promising applications of CS is compressive imaging. Leveraging the fact that images can be represented as approximately sparse signals in a transformed domain, images can be compressed and sampled simultaneously using low-complexity linear operations. Recently, these techniques have been extended beyond imaging to encode video. Much of the compression in traditional video encoding comes from using motion vectors to take advantage of the temporal correlation between adjacent frames. However, calculating motion vectors is a processing-intensive operation that causes significant power consumption. Therefore, any technique appropriate for resource constrained video sensors must exploit temporal correlation through low-complexity operations. In this tutorial, we first briefly discuss challenges involved in the transmission of video over a wireless multimedia sensor network (WMSN). We then discuss the different techniques available for applying CS encoding first to images, and then to videos for error-resilient transmission in lossy channels. Existing solutions are examined, and compared in terms of applicability to wireless multimedia sensor networks (WMSNs). Finally, open issues are discussed and future research trends are outlined.

An approach for multibit‐per‐site optical data storage has been proposed and demonstrated using mesostructured composite films incorporated with uniformly dispersed photoacid generator and pH‐sensitive dye molecules. Upon light illumination, photoacid generator molecules produce acid, which induces a change in the absorption property of pH‐sensitive dye molecules. Because the amount of the generated acid is proportional to the illumination time, the resulting change in the absorption property of mesostructured composite films varies as a function of the illumination time. This function between the absorption property of mesostructured composite films and the illumination time can be used for multibit‐per‐site optical data storage. Recording is performed by using certain discrete values of the illumination time to represent information bits. Reading out is achieved by measuring the absorbance of composite films at a particular wavelength, from which the stored information bits can be determined. In general, N‐bit‐per‐site storage can be realized using 2N discrete values of the illumination time. This multibit‐per‐site approach for optical data storage is compatible with the current single‐bit‐per‐site technology used for compact disks and digital versatile disks, and will provide significantly larger optical storage capacity. It is also suitable for two‐photon multilayer optical data storage if the photoacid generators and pH‐sensitive dyes are properly designed.

In order for rewritable optical data storage to compete with other storage technologies, it is crucial to increase the data transfer rate and storage capacity. Near-field recording should enable considerable progress in these areas, however, currently available near-field light sources are limited by their low output powers. This puts severe constraints on the thermal properties of the phase change media. This article addresses how rewritable optical data storage media can meet data transfer rate and storage capacity requirements. In particular, the required laser power for writing and erasing submicron “bits” is calculated. It is shown using modeling how the power and the write and erase times depend on the structure and thermal properties of the media, as well as on the crystallization kinetics of the recording layer. Guidelines for media optimization and areas that presently pose the most serious limitations are presented.

Electron-trapping materials have attracted a lot of attention in the field of optical data storage. However, the lack of suitable trap levels has hindered its development and application in the field of optical data storage. Herein, Lu3Al5O12:Ce3+ fluorescent ceramics were developed as the optical storage medium, and high-temperature vacuum sintering induced the formation of deep traps (1.36 eV). The matrix based on the garnet-structured material ensures excellent rewritability. By analyzing the thermoluminescence and photostimulable luminescence, it is found that the transition of electrons provided by Ce3+ between the conduction band and trap levels offers the possibility for optical data storage. As evidence of its application, the optical information encoding using 254 nm light and decoding using a light stimulus and thermal stimulus were applied. These findings are expected to provide candidate material for novel optical storage technology, and further promote the development of advanced information storage technology.

This paper presents modification of the TwIST algorithm for Compressive Sensing MRI images reconstruction. Compressive Sensing is new approach in signal processing whose basic idea is recovering signal form small set of available samples. The application of the Compressive Sensing in biomedical imaging has found great importance. It allows significant lowering of the acquisition time, and therefore, save the patient from the negative impact of the MR apparatus. TwIST is commonly used algorithm for 2D signals reconstruction using Compressive Sensing principle. It is based on the Total Variation minimization. Standard version of the TwIST uses masked 2D Discrete Fourier Transform coefficients as Compressive Sensing measurements. In this paper, different masks and different transformation domains for coefficients selection are tested. Certain percent of the measurements is used from the mask, as well as small number of coefficients outside the mask. Comparative analysis using 2D DFT and 2D DCT coefficients, with different mask shapes is performed. The theory is proved with experimental results.

In the link of satellite to ground laser communication, light signals received by ground terminal receivers are susceptible to interference due to the impact of atmospheric turbulence, and it will result in the degradation of communication quality or even interruption. In this situation, the conventional communication detection device would not be able to analyze the link performance. Thus, it is necessary to develop a set of non-continuous optical signal recognition and storage devices which could work under adverse atmospheric conditions and do the online analysis of the impact of atmospheric on the link of satellite to ground laser. Since the interruption of the optical signal will cause the logical disorders of the high data rate storage system, which uses the CDR output clock as the system clock, general storage device cannot meet the demand of effective storage for communication. In this paper, the usage of FPGA optical signal recovered clock frequency discriminator identified the impact of non-continuous communication signal of atmospheric disturbances effectively. Through the optimization of the system hardware design and software control, we achieved the identification and storage of the laser communication optical signal data while the factor of atmospheric scintillation varying from 0 to 1.5 and the communication data rate being 600Mbps.

A number of applications for high-power diode laser arrays exist, such as optical data storage, laser printing, and optical data processing, which allow enhanced operation by use of parallel, independently addressable channels. A monolithic linear array of AIGaAs diode lasers has been developed to address such applications. Pertinent considerations regarding the design, fabrication, and use of diode laser array sources include optical coupling, thermal interactions, and electrical cross talk between adjacent elements; uniformity of crystal growth; individual, independent electrical addressing of each diode; and collection and collimation of the output radiation from the laser elements. These issues are addressed, with specific examples discussed, including arrays of up to ten elements, separated by 150 µm, with each laser emitting in a fundamental spatial mode. Examples of such array based systems are shown, with particular emphasis on multichannel optical recording for high-data-rate data storage.

We discuss the light signals that are coherently emitted by some optical absorbers sub-sequent to excitation by a series of three laser excitation pulses. In some cases, the light signal emitted by the material duplicates the temporal shape of one of the laser in-put pulses. In other cases, the emitted signal represents the convolution or cross-correlation of two of the laser input pulses. In principle, digitally encoded signals of multi-gigahertz bandwidth can be convolved in real time, or, in suitable materials, stored for long periods. Output signals on the order of a few percent as intense as the inputs are possible. We provide experimental demonstrations of these effects, and discuss their implementation in optical signal processing and data storage systems.