Coded aperture compressive imaging array applied for surveillance systems

Block-wise motion detection using compressive imaging system

The newly emerging sampling methodology of compressed sensing opens a door to obtain compressed data directly. How to efficiently reconstruct the original signal from the compressed data becomes a new challenge problem. Many reconstruction works have been proposed on mono-view images by exploring the sparsity of the original image, how ever it is a challenge to efficiently explore the correlations between different views in compressed multiview imaging systems. With the aid of inter-view disparity information at receiver end, a joint reconstruction approach is presented for independently captured view point images via compressed imaging.

We have developed a compressive hyperspectral imaging system that is based on single-pixel camera architecture. We have incorporated the developed system in a scanning white-light interferometer (SWLI) and showed that replacing SWLI’s CCD-based camera by the compressive hyperspectral imaging system, we have access to high-resolution multispectral images of interferometer’s fringes. Using these multi-spectral images, the system is capable of simultaneous spectroscopy of the surface, which can be used, for example, to eliminate the effect of surface contamination and providing new spectral information for fringe signal analysis which could be used to reduce the need for vertical scan, therefore making height measurement more tolerant to object’s position.

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.

Compressive radar imaging has attracted considerable attention because it substantially reduces imaging time through directly compressive sampling. However, a problem that must be addressed for compressive radar imaging systems is the high computational complexity of reconstruction of sparse signals. In this paper, a novel algorithm, called two-dimensional (2D) normalized iterative hard thresholding (NIHT) or 2D-NIHT algorithm, is proposed to directly reconstruct radar images in the matrix domain. The reconstruction performance of 2D-NIHT algorithm was validated by an experiment on recovering a synthetic 2D sparse signal, and the superiority of the 2D-NIHT algorithm to the NIHT algorithm was demonstrated by a comprehensive comparison of its reconstruction performance. Moreover, to be used in compressive radar imaging systems, a 2D sampling model was also proposed to compress the range and azimuth data simultaneously. The practical application of the 2D-NIHT algorithm in radar systems was validated by recovering two radar scenes with noise at different signal-to-noise ratios, and the results showed that the 2D-NIHT algorithm could reconstruct radar scenes with a high probability of exact recovery in the matrix domain. In addition, the reconstruction performance of the 2D-NIHT algorithm was compared with four existing efficient reconstruction algorithms using the two radar scenes, and the results illustrated that, compared to the other algorithms, the 2D-NIHT algorithm could dramatically reduce the computational complexity in signal reconstruction and successfully reconstruct 2D sparse images with a high probability of exact recovery.

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.

Compressive imaging (CI) is a natural branch of compressed sensing (CS). Although a number of CI implementations have started to appear, the design of efficient CI system still remains a challenging problem. One of the main difficulties in implementing CI is that it involves huge amounts of data, which has far-reaching implications for the complexity of the optical design, calibration, data storage and computational burden. In this paper, we solve these problems by using a two-dimensional separable sensing operator. By so doing, we reduce the complexity by factor of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> for megapixel images. We show that applying this method requires only a reasonable amount of additional samples.

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.

Clinical evaluation of wavelet-compressed digitized screen-film mammography

Imaging through a multimode fiber (MMF) with a spatial-resolution beyond the diffraction limit has recently been demonstrated using computational super-resolution methods. We performed a modelling study to assess the performance of a compressed image reconstruction algorithm, Basis Pursuit, using different illuminations. In addition to the increased speed due to the reduced number of measurements, we characterized other potential benefits with respect to robustness to noise and resilience to fiber bending when using compressed imaging with optimized illuminations.

Compressive hyperspectral imaging is based on the fact that hyperspectral data is highly redundant. However, there is no symmetry between the compressibility of the spatial and spectral domains, and that should be taken into account for optimal compressive hyperspectral imaging system design. Here we present a study of the influence of the ratio between the compression in the spatial and spectral domains on the performance of a 3D separable compressive hyperspectral imaging method we recently developed.

In this work, we present a novel camera array that exploits the electronic rolling shutter to achieve high speed compressive temporal imaging. Traditional compressive temporal imaging makes use of mechanical coded apertures, despite implementation and calibration challenges. Instead, we propose to model the inherent spatial and temporal coding provided by the distinctive rolling shutter sampling from each camera of the array as a compressive temporal imaging system matrix. Thus, we can recover a high speed video from a set of snapshots from the camera array by using compressive sensing reconstruction algorithms. We present both simulation and experimental results for a 4-camera array system with different orientation angles, reconstructing up to 56 high-speed sub-frames from a set of simultaneously triggered snapshots from the array, achieving a compression rate of up to 14X.

Provable deep video denoiser using spatial–temporal information for video snapshot compressive imaging: Algorithm and convergence analysis

this study presents an eight array ultrasonic signal phased array sparse sampling experiment system based ultrasonic phased array technology and Compressed Sensing (CS). Proposed system considers recovery ultrasonic beam signal received eight phased array elements with sparse samples captured using sub-Nyquist model in CS recovery algorithm. We have the block defect detection test in the system. The test result approximated the actual block defect position. Based on block defect detection test, We compared sparse sampling value using spectrum estimation to Compressed Sensing recovery algorithm imaging, and no focus and focus detection effect, proved the phased array experiment system based on Compressed Sensing .it can greatly improve the detection signal to noise ratio (SNR) and sensitivity. So we verify the phased array focus can improve the detection ability.

Compressive holography estimates images from incomplete data by using sparsity priors. Compressive holography combines digital holography and compressive sensing. Digital holography consists of computational image estimation from data captured by an electronic focal plane array. Compressive sensing enables accurate data reconstruction by prior knowledge on desired signal. Computational and optical co-design optimally supports compressive holography in the joint computational and optical domain. This dissertation explores two examples of compressive holography: estimation of 3D tomographic images from 2D data and estimation of images from under sampled apertures. Compressive holography achieves single shot holographic tomography using decompressive inference. In general, 3D image reconstruction suffers from underdetermined measurements with a 2D detector. Specifically, single shot holographic tomography shows the uniqueness problem in the axial direction because the inversion is ill-posed. Compressive sensing alleviates the ill-posed problem by enforcing some sparsity constraints. Holographic tomography is applied for video-rate microscopic imaging and diffuse object imaging. In diffuse object imaging, sparsity priors are not valid in coherent image basis due to speckle. So incoherent image estimation is designed to hold the sparsity in incoherent image basis by support of multiple speckle realizations. High pixel count holography achieves high resolution and wide field-of-view imaging. Coherent aperture synthesis can be one method to increase the aperture size of a detector. Scanning-based synthetic aperture confronts a multivariable global optimization problem due to time-space measurement errors. A hierarchical estimation strategy divides the global problem into multiple local problems with support of computational and optical co-design. Compressive sparse aperture holography can be another method. Compressive sparse sampling collects most of significant field information with a small fill factor because object scattered fields are locally redundant. Incoherent image estimation is adopted for the expanded modulation transfer function and compressive reconstruction.