Compressive Imaging System Research Articles

Sparse Reconstruction of Compressive Sensing Multi-Spectral Data Using an Inter-Spectral Multi-Layered Conditional Random Field Model

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.

并行压缩成像系统的压缩域小目标检测

并行压缩成像系统的压缩域小目标检测

Coded aperture optimization for compressive X-ray tomosynthesis.

Radiation dose is a concern in X-ray tomographic imaging; coded aperture compressive X-ray tomosynthesis is an approach used to reduce radiation. It places a coded aperture in front of an X-ray source in order to obtain 2D patterned projections of a three-dimensional object onto a detector plane. By using different coded apertures in a multiple source system, multiplexed projections can be obtained instead of sequential projections as in conventional tomosynthesis systems. Compressed sensing (CS) reconstruction algorithms are then used to recover the three-dimensional data cube. An optimization approach to design the structure of the coded apertures in a multiple source compressive X-ray tomosynthesis imaging system is presented. A uniform energy criteria on the voxels and detector elements is used so that the object is uniformly sensed and the elements of the detector plane uniformly sense the information. Simulations and experimental results for optimized coded apertures are shown, and their performance is compared to the use of random coded apertures.

Aliasing Influence Reduction Method for the Estimation of Compressive Imaging System's Modulation Transfer Function

Aliasing Influence Reduction Method for the Estimation of Compressive Imaging System's Modulation Transfer Function

I2.1: Invited Paper: On the Duality of Compressive Light Field Imaging and Display

In the recent computational imaging and display literature, light field cameras and displays are being treated distinctly, without an obvious connection. Despite significant differences in the signal processing tools employed in compressive light field imaging systems, we argue that there is a natural duality between these systems. Following Gabriel Lippmann's vision of integrated light field cameras and displays, we develop an intuitive interpretation of the optical systems and compressive optimization schemes employed in modern compressive light field imaging systems.

Imaging method with low data rate for autonomous star trackers based on compressive sensing

Compressive sensing (CS) theory is popular and has led to the development of new imaging methods in many fields. Specifically, CS theory can reduce the sampling rate of a high-accuracy star tracker and maintain the quality of star images. However, the imaging system of a star tracker contains certain properties that cannot correspond to conventional measurement architectures. Thus, a compressive imaging system that is suitable for the star trackers used in space missions is proposed. First, the CS framework is briefly introduced and the feasibility of typical measurement architectures is analyzed. Second, an imaging method with a low data rate and based on deterministic phase modulation (DPM) is presented to form a weight block circulant matrix (WBCM) of deterministic measurement. The advantage of the proposed imaging method is determined by comparing it with the typical measurement architectures. Third, numerical simulations are conducted to investigate the performance of the proposed method. Results show that the reconstruction quality of the DPM architecture is close to that of random phase modulation architecture. Meanwhile, the WBCM measurement is better than the conventional block circulant matrix measurement. Furthermore, the statistical result significantly ensures accurate star centroid estimation and confirms the feasibility of CS application to star tracker imaging at low data rates.

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

The measurement matrix employed in compressive sensing typically cannot be known precisely a priori and must be estimated via calibration. One may take multiple compressive measurements, from which the measurement matrix and underlying signals may be estimated jointly. This is of interest as well when the measurement matrix may change as a function of the details of what is measured. This problem has been considered recently for Gaussian measurement noise, and here we develop this idea with application to Poisson systems. A collaborative maximum likelihood algorithm and alternating proximal gradient algorithm are proposed, and associated theoretical performance guarantees are established based on newly derived concentration-of-measure results. A Bayesian model is then introduced, to improve flexibility and generality. Connections between the maximum likelihood methods and the Bayesian model are developed, and example results are presented for a real compressive X-ray imaging system.

Multi-Scale Compressed Ghost Imaging System

Fixed-scale speckles are used in existing ghost imaging system, while the current instrument can achieve multi- scale speckles ghost imaging. This paper focuses on the research of compressed sensing ghost imaging system using multi-scale speckles. Through the computer simulation and using the four resolution scales image as the original image, restored images are obtained to analysis on a variety of different scales and different proportion of speckles cases. The results show that the compressed sensing ghost imaging system using multi-scale speckles can get closer to the true value of image. And when the small scale speckles occupy large proportion, the recovered image resolution is not disturbed.

Spectral resolution enhancement of hyperspectral imagery by a multiple-aperture compressive optical imaging system

The Coded Aperture Snapshot Spectral Imaging (CASSI) system captures the three-dimensional (3D) spatio-spectral information of a scene using a set of two-dimensional (2D) random-coded Focal Plane Array (FPA) measurements. A compressive sensing reconstruction algorithm is then used to recover the underlying spatio-spectral 3D data cube. The quality of the reconstructed spectral images depends exclusively on the CASSI sensing matrix, which is determined by the structure of a set of random coded apertures. In this paper, the CASSI system is generalized by developing a multiple-aperture optical imaging system such that spectral resolution enhancement is attainable. In the proposed system, a pair of high-resolution coded apertures is introduced into the CASSI system, allowing it to encode both spatial and spectral characteristics of the hyperspectral image. This approach allows the reconstruction of superresolved hyperspectral data cubes, where the number of spectral bands is significantly increased and the quality in the spatial domain is greatly improved. Extensively simulated experiments show a gain in the peak-signal-to-noise ratio (PSNR), along with a better fit of the reconstructed spectral signatures to the original spectral data.

Single-pixel optical sensing architecture for compressive hyperspectral imaging

Compressive hyperspectral imaging systems (CSI) capture the threedimensional (3D) information of a scene by measuring two-dimensional (2D) coded projections in a Focal Plane Array (FPA). These projections are then exploited by means of an optimization algorithm to obtain an estimation of the underlying 3D information. The quality of the reconstructions is highly dependent on the resolution of the FPA detector, which cost grows exponentially with the resolution. High-resolution low-cost reconstructions are thus desirable. This paper proposes a Single Pixel Compressive Hyperspectral Imaging Sensor (SPHIS) to capture and reconstruct hyperspectral images. This optical architecture relies on the use of multiple snapshots of two timevarying coded apertures and a dispersive element. Several simulations with two different databases show promising results as the reliable reconstruction of a hyperspectral image can be achieved by using as few as just the 30% of its voxels.

Single exposure compressed imaging system with Hartmann-Shack wavefront sensor

A new compressed imaging system based on compressed sensing (CS) theory is proposed. One single exposure with a frame sensor can replace a sequence of measurements, which is necessary in the conventional CS imaging systems. First, the phase of the incident light is randomly modulated in the Fourier transform domain using a spatial light modulator. When the modulated light passes through the inverse Fourier transform lens, the information of the optical field will spread out across the entire modulated image. Then, a Hartmann-Shack wavefront sensor is employed to sense the intensity and phase information in the final imaging plane. The resolution of the Hartmann-Shack wavefront sensor is far less than the inherent resolution of the imaging system. Finally, a high-resolution image can be reconstructed from the image partially sampled from the Hartmann-Shack wavefront sensor at any position. The numerical experiments demonstrate the effectiveness of the proposed imaging method.

Compressive acoustic imaging with metamaterials

Compressive imaging has brought revolutionary design methodologies to imaging systems. By shuffling and multiplexing the object information space, the imaging system compresses data on the physical layer and enables employing fewer sensors and acquiring less data than traditional isomorphic mapping imaging systems. Recently metamaterials have been investigated for designing compressive imager. Metamaterials are engineered materials with properties that are usually unattainable in nature. Acoustic metamaterials can possess highly anisotropy, strongly dispersion, negative dynamic density, or bulk modulus, and they open up new possibilities of wave-matter interaction and signal modulation. In this work, we designed, fabricated, and tested a metamaterial-based single detector, 360 degree field of view compressive acoustic imager. Local resonator arrays are design to resonate randomly in both spatial and spectrum dimensions to favor compressive imaging task. The presented experimental results show that with only about 60 measured values, the imager is able to reconstruct a scene of more than 1000 sampling points in space, achieving a compression ratio of about 20:1. Multiple static and moving target imaging task were performed with this low cost, single detector, non-mechanical scanning compressive imager. Our work paves the way for designing metamaterials based compressive acoustic imaging system.

Efficient Compressive Multi-Focus Image Fusion

Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed in literatures. However, the traditional clarity measures are not designed for compressive imaging measurements which are maps of source sense with random or likely ran- dom measurements matrix. This paper presents a novel efficient multi-focus image fusion frame- work for compressive imaging sensor network. Here the clarity measure of the raw compressive measurements is not obtained from the random sampling data itself but from the selected Hada- mard coefficients which can also be acquired from compressive imaging system efficiently. Then, the compressive measurements with different images are fused by selecting fusion rule. Finally, the block-based CS which coupled with iterative projection-based reconstruction is used to re- cover the fused image. Experimental results on common used testing data demonstrate the effectiveness of the proposed method.

Coded aperture compressive imaging array applied for surveillance systems

This paper proposes an application of compressive imaging systems to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system and a corresponding motion target detection algorithm in video using compressive image data are developed. Coded masks with random Gaussian, Toeplitz and random binary are utilized to simulate the compressive image respectively. For compressive images, a mixture of the Gaussian distribution is applied to the compressed image field to model the background. A simple threshold test in compressive sampling image is used to declare motion objects. Foreground image retrieval from underdetermined measurement using the total variance optimization algorithm is explored. The signal-to-noise ratio (SNR) is employed to evaluate the image quality recovered from the compressive sampling signals, and receiver operation characteristic (ROC) curves are used to quantify the performance of the motion detection algorithm. Experimental results demonstrate that the low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz mask, motion detection algorithms using the random binary phase mask can yield better detection results. However using the random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed images.

Spatial super-resolution in coded aperturebased optical compressive hyperspectral imaging systems

The Coded Aperture Snapshot Spectral Imaging system (CASSI) is a remarkable optical imaging architecture, which senses the spectral information of a three dimensional scene by using two-dimensional coded focal plane array (FPA) projections. The projections in CASSI are localized such that each measurement contains spectral information only from a specific spatial region of the data cube. Spatial resolution in CASSI is highly dependent on the resolution the FPA detector exhibits; hence, high-resolution images require high-resolution detectors that demand high costs. To overcome this problem, in this paper is proposed an optical model for spatial superresolution imaging called SR-CASSI. Spatial super-resolution is attained as an inverse problem from a set of low-resolution coded measurements by using a compressive sensing (CS) reconstruction algorithm. This model allows the reconstruction of spatially super-resolved hyper-spectral data cubes, where the spatial resolution is significantly enhanced. Simulation results show an improvement of up to 8 dB in PSNR when the proposed model is used.

Characterization of a compressive imaging system using laboratory and natural light scenes

Compressive imagers acquire images, or other optical scene information, by a series of spatially filtered intensity measurements, where the total number of measurements required depends on the desired image quality. Compressive imaging (CI) offers a versatile approach to optical sensing which can improve size, weight, and performance (SWaP) for multispectral imaging or feature-based optical sensing. Here we report the first (to our knowledge) systematic performance comparison of a CI system to a conventional focal plane imager for binary, grayscale, and natural light (visible color and infrared) scenes. We generate 1024×1024 images from a range of measurements (0.1%-100%) acquired using digital (Hadamard), grayscale (discrete cosine transform), and random (Noiselet) CI basis sets. Comparing the outcome of the compressive images to conventionally acquired images, each made using 1% of full sampling, we conclude that the Hadamard Transform offered the best performance and yielded images with comparable aesthetic quality and slightly higher spatial resolution than conventionally acquired images.

Quantization error and dynamic range considerations for compressive imaging systems design

A natural field of application for compressive sensing theory is imaging. Indeed, numerous compressive imaging (CI) systems and applications have been developed during the last few years. This work addresses the quantization effect in CI, which is fundamental for most CI architectures. In this paper, the implications of sensor quantization on universal CI are investigated theoretically and demonstrated with numerical experiments. It is shown that employing a CI framework may set severe requirements on the quantization depth of the optical sensor used. The quantization depth overhead requirement may be prohibitive in many optical imaging scenarios employing typical CI architectures. Practical solutions that significantly alleviate this requirement are suggested.

压缩采样光谱调制技术研究

The compressive sampling spectral modulated technique is developed in order to avoid time penalty and promote throughput of spectral imaging system,and a digital-micromirror-device based compressive sampling multispectral imaging system is designed.In the technique based compressive sampling spectral information is sampled below Nyquist-criterion with coded aperture to reduce spectral data greatly.In the experiment,the cubic spatial/spectral data cubes of the 612 nm laser and colored striped fabric are reconstructed from 2-Dimension images obtained from detector that contain spectral information using double shrinkage fast iterative algorithm.The results indicate that a digital-micromirror-device based compressive sampling multispectral imaging system has images with high-throughput and high resolution,can compress the spectral simultaneously,and the compression ratio can reach 31∶1.

A Coded Aperture Compressive Imaging Array and Its Visual Detection and Tracking Algorithms for Surveillance Systems

In this paper, we propose an application of a compressive imaging system to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system is proposed to reduce the needed high resolution coded mask requirements and facilitate the storage of the projection matrix. Random Gaussian, Toeplitz and binary phase coded masks are utilized to obtain the compressive sensing images. The corresponding motion targets detection and tracking algorithms directly using the compressive sampling images are developed. A mixture of Gaussian distribution is applied in the compressive image space to model the background image and for foreground detection. For each motion target in the compressive sampling domain, a compressive feature dictionary spanned by target templates and noises templates is sparsely represented. An l1 optimization algorithm is used to solve the sparse coefficient of templates. Experimental results demonstrate that low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz phase mask, motion detection algorithms using a random binary phase mask can yield better detection results. However using random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed image. Our tracking algorithm can achieve a real time speed that is up to 10 times faster than that of the l1 tracker without any optimization.

Object reconstruction in block-based compressive imaging

A block-based compressive imaging (BCI) system using sequential architecture is presented in this paper. Feature measurements are collected using the principal component analysis (PCA) projection. The linear Wiener operator and a nonlinear method based on the Field-of-Expert (FoE) prior model are used for object reconstruction. Experimental results are given to demonstrate the superior reconstruction performance of the FoE-based method over the Wiener operator. In addition, the effects of system parameters, such as the object block size, the number of features per block, and the noise level to the BCI reconstruction performance are discussed with different kinds of objects. Then an optimal block size is defined and studied for BCI.