Compressive temporal imaging using a rolling shutter camera array.

Coded aperture compressive imaging array applied for surveillance systems

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.

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 combination of single-pixel-imaging and single-photon-counting technology can achieve ultrahigh-sensitivity photon-counting imaging. However, its applications in high-resolution and real-time scenarios are limited by the long sampling and reconstruction time. Deep-learning-based compressive sensing provides an effective solution due to its ability to achieve fast and high-quality reconstruction. This paper proposes a sampling and reconstruction integrated neural network for single-photon-counting compressive imaging. To effectively remove the blocking artefact, a subpixel convolutional layer is jointly trained with a deep reconstruction network to imitate compressed sampling. By modifying the forward and backward propagation of the network, the first layer is trained into a binary matrix, which can be applied to the imaging system. An improved deep-reconstruction network based on the traditional Inception network is proposed, and the experimental results show that its reconstruction quality is better than existing deep-learning-based compressive sensing reconstruction algorithms.

Block-wise motion detection using compressive imaging system

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.

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

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.

Traditional single-pixel imaging systems are aimed mainly at relatively static or slowly changing targets. When there is relative motion between the imaging system and the target, sizable deviations between the measurement values and the real values can occur and result in poor image quality of the reconstructed target. To solve this problem, a novel dynamic compressive imaging system is proposed. In this system, a single-column digital micro-mirror device is used to modulate the target image, and the compressive measurement values are obtained for each column of the image. Based on analysis of the measurement values, a new recovery model of dynamic compressive imaging is given. Differing from traditional reconstruction results, the measurement values of any column of vectors in the target image can be used to reconstruct the vectors of two adjacent columns at the same time. Contingent upon characteristics of the results, a method of image quality enhancement based on an overlapping average algorithm is proposed. Simulation experiments and analysis show that the proposed dynamic compressive imaging can effectively reconstruct the target image; and that when the moving speed of the system changes within a certain range, the system reconstructs a better original image. The system overcomes the impact of dynamically changing speeds, and affords significantly better performance than traditional compressive imaging.

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 develop a high-speed compressive Raman imaging technology using a programmable binary spectral filter and a single channel detector to perform fast Raman imaging for detection and concentration estimation of know species over millimeters field of view. The technology enables Raman imaging with a pixel dwell time as short as few hundreds of microseconds. We report fast Raman imaging of pharmaceutical tablets and micro-plastics. We also present a novel fast line scan compressive Raman imaging scheme using the 2D digital micro-mirror device (DMD) to encode both space and frequency.

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 hyperspectral imaging systems (CSI) capture the three-dimensional (3D) information of a scene by measuring two dimensional (2D) using a small set of coded focal plane array (FPA) compressive measurement. A reconstruction algorithm takes advantage of the compressive measurements sparsity to recover the 3D data cube. Traditionally, CASSI uses block-unblock coded apertures to spatially modulate the light, the modulation has binary entries. In CASSI the quality of the reconstructed images depends on the design of these coded apertures and the FPA saturation. This work presents a new CASSI architecture based on grayscaled coded apertures (GCA) which reduce the saturation and increase the dynamic range of the FPA detector. The set of codes is calculated in a realtime adaptive manner such that the FPA compressive measurements are used to determine the structure of the GCA. Simulations show the improvement in the quality of the reconstructed images of the architecture based on GCA.

We present an end-to-end deep learning model designed to reconstruct up to eight frames from a single snapshot of a dynamic object passing through an unknown, time-varying scattering medium. Our approach integrates a coded aperture compressive temporal imaging system with a specially designed transformer-based convolutional neural network (CNN), optimized for effective demultiplexing and reconstruction. Both simulation and experimental results demonstrate a successful compression ratio of up to 8X, while maintaining high reconstruction quality. Furthermore, ablation studies reveal that our dual-input CNN model, which utilizes both speckle patterns and their autocorrelations, significantly improves reconstruction accuracy.

Scenes in real world have dynamic range of radiation that cannot be captured by conventional cameras. High dynamic range imaging is a technique to capture detail images where, in the field of image, intensity variation is extreme. This technique is very useful for biological imaging where the samples have very bright and very dark regions and both parts have useful information. In this article we propose a novel high dynamic range imaging technique based on compressive imaging that uses one single detector instead of camera (array of detectors) to capture an image. Combination of high dynamic range imaging and compressive imaging benefits from imaging with high dynamic range of radiation and advantages of compressive sampling; namely, imaging at regions of optical spectrum where conventional cameras are not readily available and single detectors are available. Additionally, as its name suggests, this technique requires less number of samples (compared to raster scanning). Our experimental results show that high dynamic range compressive imaging system is capable of capturing images with large intensity contrast.