Compressive Imaging System Research Articles

Adaptive Single Photon Compressed Imaging Based on Constructing a Smart Threshold Matrix.

We demonstrate a single-photon compressed imaging system based on single photon counting technology and compressed sensing theory. In order to cut down the measurement times and shorten the imaging time, a fast and efficient adaptive sampling method, suited for single-photon compressed imaging, is proposed. First, the pre-measured rough images are transformed into sparse bases as a priori information. Then a smart threshold matrix is designed by using large sparse coefficients of the rough image in sparse bases. The adaptive measurement matrix is obtained by modifying the original Gaussian random matrix with the specially designed threshold matrix. Building the adaptive measurement matrix requires only one level of sparse representation, which means that adaptive imaging can be achieved quickly with very little computation. The experimental results show that the reconstruction effect of the image measured using the adaptive measurement matrix is obviously superior than that of the Gaussian random matrix under different measurement times and different reconstruction algorithms.

Rank Minimization for Snapshot Compressive Imaging.

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications. This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.

Compressive spectral imaging system based on liquid crystal tunable filter.

Liquid crystal tunable filters (LCTF) are extensively used in hyperspectral imaging systems to successively acquire different spectral components of scenes by adjusting the center wavelength of the filter. However, the spectral and spatial resolutions of the imager are limited by the bandwidth of LCTF, and the pitch dimension of the detector, respectively. This paper applies compressive sensing principles to improve both of the spatial and spectral resolutions of the LCTF-based hyperspectral imaging system. An accurate transmission model of the LCTF is used to represent its bandpass filtering effects on the spectra. In addition, a random coded aperture placed behind the LCTF is used to modulate the spectral images in the spatial domain. Then, the three-dimensional encoded spectral images are projected onto a two-dimensional detector. Benefiting from the spectral-dependent transmission property of the LCTF, information of the entire spectrum is collected by a few snapshots using different center wavelengths of the LCTF. Super-resolution hyperspectral images can be reconstructed from a small set of compressive measurements by solving a convex optimization problem. Simulations and experiments show that the proposed method can effectively improve the spectral and spatial resolutions of traditional LCTF-based spectral imager without changing the structures of the LCTF and detector. Finally, a multi-channel spectral coding method is proposed to further increase the compression capacity of the system.

Large-area single photon compressive imaging based on multiple micro-mirrors combination imaging method

We demonstrate a single photon compressive imaging system with the image plane up to the entire digital micro-mirror device (DMD) work area. A parallel light source is designed to reduce the influence of light scattering on imaging resolution and a photon counting photomultiplier tube (PMT) with a large photosensitive area is used to effectively collect light reflected from the full screen of DMD. A control and counting circuit, based on Field-Programmable Gate Array (FPGA), is developed to load binary random matrix into the DMD controller for each measurement, and to count single-photon pulse output from PMT simultaneously. To reduce imaging time and huge memory occupation for image reconstruction, a multiple micro-mirrors combination imaging method is proposed. The signal-to-noise ratio and detection limit of the imaging system is theoretically deduced. Theoretical analysis and experimental results show that micro-mirrors combination imaging method is more suitable for faster imaging in a weaker-light-level environment. In order to achieve high imaging quality, the size of the combined pixels and the average time of each measurement should be moderate, so that the impact of Poisson shot noise is minimized.

Compressive hyperspectral imaging recovery by spatial-spectral non-local means regularization.

Hyperspectral imaging systems can benefit from compressed sensing to reduce data acquisition demands. We present a new reconstruction algorithm to recover the hyperspectral datacube from limited optically compressed measurements, exploiting the inherent spatial and spectral correlations through non-local means regularization. The reconstruction process is solved with the help of split Bregman optimization techniques, including penalty functions defined according to the spatial and spectral properties of the scene and noise sources. For validation purposes, we also implemented a compressive hyperspectral imaging system that relies on a digital micromirror device and a near-infrared spectrometer, where we obtained enhanced and promising reconstruction results when using our proposed technique in contrast with traditional compressive image reconstruction.

Parallel lensless compressive imaging via deep convolutional neural networks.

We report a parallel lensless compressive imaging system, which enjoys real-time reconstruction using deep convolutional neural networks. A prototype composed of a low-cost LCD, 16 photo-diodes and isolation chambers, has been built. Each of these 16 channels captures a fraction of the scene with 16×16 pixels and they are performing in parallel. An efficient inversion algorithm based on deep convolutional neural networks is developed to reconstruct the image. We have demonstrated encouraging results using only 2% (relative to pixel numbers, e.g. 5 for a block with 16×16 pixels) measurements per sensor for digits and around 10% measurements per sensor for facial images.

Compressive high-speed stereo imaging.

A compressive high-speed stereo imaging system is reported. The system is capable of reconstructing 3D videos at a frame rate 10 times higher than the sampling rate of the imaging sensors. An asymmetric configuration of stereo imaging system has been implemented by including a high-speed spatial modulator in one of the binocular views, and leaving the other view unchanged. We have developed a two-step reconstruction algorithm to recover the irradiance and depth information of the high-speed scene. The experimental results have demonstrated high-speed video reconstruction at 800fps from 80fps measurements. The reported compressive stereo imaging method does not require active illumination, offering a robust yet inexpensive solution to high-speed 3D imaging.

2D Normalized Iterative Hard Thresholding Algorithm for Fast Compressive Radar Imaging

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.

A quantitative and qualitative performance analysis of compressive spectral imagers

Contexto: Las imágenes espectrales (SI) contienen información espacio-espectral acerca de una escena disponible en un cubo de datos que usualmente comprende una cantidad significativa de éstos. Los sistemas tradicionales de (SI) adquieren datos redundantes ignorando la alta correlación entre las mediciones y las muestras redundantes. Los sistemas de compresión de imágenes espectrales comprimen los datos espectrales en la etapa de adquisición, lo que permite reducir la cantidad de datos y la redundancia. Actualmente, existen varios sistemas de imágenes espectrales disponibles que proporcionan nuevas funciones para los usuarios y abren un amplio campo de nuevas aplicaciones. Por ejemplo, los sistemas de CASSI, SCSI, SSCS, y HYCA son cuatro de los más destacados.Método: La revisión de algunos trabajos provee amplios estudios de tecnologías disponibles y muestra cómo se pueden utilizar las nuevas capacidades de los enfoques de formación de imágenes espectrales. Sin embargo, para la selección de una arquitectura específica se requiere una comparación cuantitativa y cualitativa de estos sistemas en los mismos escenarios.Resultados: En este trabajo se analiza el rendimiento cualitativo y cuantitativo de estos cuatro sistemas de compresión de imágenes espectrales para evaluarlos en los mismos escenarios. Para ello, cada arquitectura se modela como un sistema de ecuaciones lineales y el proceso de reconstrucción de las imágenes se logra con el mismo enfoque de optimización transmitancia, código de apertura y número de proyecciones.Conclusión: Se muestra que el sistema SSCSI alcanza el mejor rendimiento en la reconstrucción con el valor más alto PSNR.

Compressive video sensing with side information.

Our temporally compressive imaging system reconstructs a high-speed image sequence from a single, coded snapshot. The reconstruction quality, similar to that of other compressive sensing systems, often depends on the structure of the measurement, as well as the choice of regularization. In this paper, we report a compressive video system that also captures the side information to aid in the reconstruction of high-speed scenes. The integration of the side information not only improves the quality of reconstruction, but also reduces the dependence of the reconstruction on regularization. We have implemented a system prototype that splits the field of view of a single camera into two channels: one channel captures the coded, low-frame-rate measurement for high-speed video reconstruction, and the other channel captures a direct measurement without coding as the side information. A joint reconstruction model is developed to recover the high-speed videos from the two channels. By analyzing both the experimental and the simulation results, the reconstructions with side information have demonstrated superior performances in terms of both the peak signal-to-noise ratio and structural similarity.

High-Speed Compressive Microscopy of Flowing Cells Using Sinusoidal Illumination Patterns

Single-pixel imaging (SPI) exploits the sparsity structure of natural images and enables image acquisition with far fewer measurements than the number of pixels. However, current SPI devices suffer from low imaging speed and an extremely time-consuming image reconstruction operation. Photonic time-stretch (PTS) technique is demonstrated to enable high-speed structured-illumination pattern generation, which significantly accelerates the imaging speed of conventional SPI devices. Meanwhile, to achieve fast image reconstruction, phase-shifted sinusoidal illumination patterns are used to acquire the Fourier spectrum of images and then a simple inverse fast Fourier transform (IFFT) operation is required for image reconstruction, which is quite computationally efficient. This paper presents a high-speed real-time compressive imaging system and its applications to imaging flow cytometry. Not only is a 625-kHz frame rate demonstrated, which is three orders of magnitude higher than that of a conventional SPI device, but the reconstruction process is remarkably accelerated as well, which enables our prototype system to operate in real time.

并行可见光焦平面压缩成像系统

并行可见光焦平面压缩成像系统

Compressive spectral testbed imaging system based on thin-film color-patterned filter arrays.

Compressive spectral imaging systems can reliably capture multispectral data using far fewer measurements than traditional scanning techniques. In this paper, a thin-film patterned filter array-based compressive spectral imager is demonstrated, including its optical design and implementation. The use of a patterned filter array entails a single-step three-dimensional spatial-spectral coding on the input data cube, which provides higher flexibility on the selection of voxels being multiplexed on the sensor. The patterned filter array is designed and fabricated with micrometer pitch size thin films, referred to as pixelated filters, with three different wavelengths. The performance of the system is evaluated in terms of references measured by a commercially available spectrometer and the visual quality of the reconstructed images. Different distributions of the pixelated filters, including random and optimized structures, are explored.

Compressive Estimation and Imaging Based on Autoregressive Models.

Compressed sensing (CS) is a fast and efficient way to obtain compact signal representations. Oftentimes, one wishes to extract some information from the available compressed signal. Since CS signal recovery is typically expensive from a computational point of view, it is inconvenient to first recover the signal and then extract the information. A much more effective approach consists in estimating the information directly from the signal's linear measurements. In this paper, we propose a novel framework for compressive estimation of autoregressive (AR) process parameters based on ad hoc sensing matrix construction. More in detail, we introduce a compressive least square estimator for AR(p) parameters and a specific AR(1) compressive Bayesian estimator. We exploit the proposed techniques to address two important practical problems. The first is compressive covariance estimation for Toeplitz structured covariance matrices where we tackle the problem with a novel parametric approach based on the estimated AR parameters. The second is a block-based compressive imaging system, where we introduce an algorithm that adaptively calculates the number of measurements to be acquired for each block from a set of initial measurements based on its degree of compressibility. We show that the proposed techniques outperform the state-of-the-art methods for these two problems.

Multiple-Image Encryption Based on Compressive Ghost Imaging and Coordinate Sampling

A multiple-image encryption method that is based on a modified logistic map algorithm, compressive ghost imaging, and coordinate sampling is proposed. In the encryption process, random phase-only masks are first generated with the modified logistic map algorithm; multiple secret images are transformed to be sparsed by the 2-D discrete cosine transformation (DCT) operation and scrambled by different random sequences; the scrambled images are then grouped to one combined image with the help of the coordinate sampling matrices; finally, putting the combined image in the object plane of the compressive ghost imaging system, the ciphertext will be obtained from the buck detector and transferred to the receivers. During the decryption, which is the possession of the correct principle keys and the assistant geometrical parameter key, the original secret images can be successfully decrypted by the logistic map algorithm, measurement matrix generations, compressive sensing reconstruction, DCT distribution extraction, inverse DCT transform, and filtering operation.

COMPRESSED SYNTHETIC APERTURE RADAR IMAGING BASED ON MAXWELL EQUATION

Within few years backward, researches had presented the ability of compressive sensing to handle the large data problem on high resolution synthetic aperture radar (SAR) imaging. The main issue on CS framework that should be dealt with the SAR imaging is on the requirement of linearization on the measurement system. This paper proposes a new approach on formulating the compressed SAR echo imaging system which is derived from the Maxwell’s equations with continuous signal along the SAR antenna movement. Born approximation is applied to approximate the linear form of the SAR echo imaging system. In addition, the compressed sampling is formed by reducing the sampling rate of received radar signals randomly simultaneously on both of low sampling of fast time and slow time signals and by reducing the pulse period interval of transmitted signals. The simulation’s result shows that a better focused reconstructed sparse target can be achieved compared with the conventional match filter based Range Doppler (RD) method.

A Compressive System Matrix Design in Spectral Imaging by a Homogenization Algorithm

Context: Compressive spectral imaging systems (CSI) use a focal plane array (FPA) to measure two-dimensional (2D) coded projections of a three-dimensional (3D) spatiospectral scene. A reconstruction algorithm based on compressive sensing theory exploits the projections to retrieve the underlying 3D scene. Compressive sensing relies on two principles: Sparsity and incoherence. Higher incoherence drives to better-reconstructed image quality. In CSI systems, a random design of the coded apertures elements guarantees a high incoherence between the sensing matrix and the representation basis. However, when a coded aperture is designed completely random, it is possible that some voxels not be sensed at all or they be sensed more than once. Method: This paper presents a random algorithm for a colored coded apertures design by homogenizing defined parameters of the colored coded aperture snapshot spectral imaging system (C-CASSI) representative matrix. Homogenization parameters guarantee that all voxels are sensed at least once. The homogenization is achieved by weighting the selected parameters of the matrix, in this case, the average of unblocking elements per column and the average of unblocking elements per row. Results/Conclusions: Simulations show a higher performance in the PSNR of the reconstructed images by using the proposed approach, as compared to traditional random coded apertures.

Fast compressive measurements acquisition using optimized binary sensing matrices for low-light-level imaging.

Compressive measurements benefit low-light-level imaging (L3-imaging) due to the significantly improved measurement signal-to-noise ratio (SNR). However, as with other compressive imaging (CI) systems, compressive L3-imaging is slow. To accelerate the data acquisition, we develop an algorithm to compute the optimal binary sensing matrix that can minimize the image reconstruction error. First, we make use of the measurement SNR and the reconstruction mean square error (MSE) to define the optimal gray-value sensing matrix. Then, we construct an equality-constrained optimization problem to solve for a binary sensing matrix. From several experimental results, we show that the latter delivers a similar reconstruction performance as the former, while having a smaller dynamic range requirement to system sensors.

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