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An essential reference for optical sensor system design This is the first text to present an integrated view of the optical and mathematical analysis tools necessary to understand computational optical system design. It presents the foundations of computational optical sensor design with a focus entirely on digital imaging and spectroscopy. It systematically covers: Coded aperture and tomographic imaging Sampling and transformations in optical systems, including wavelets and generalized sampling techniques essential to digital system analysis Geometric, wave, and statistical models of optical fields The basic function of modern optical detectors and focal plane arrays Practical strategies for coherence measurement in imaging system design The sampling theory of digital imaging and spectroscopy for both conventional and emerging compressive and generalized measurement strategies Measurement code design Linear and nonlinear signal estimation The book concludes with a review of numerous design strategies in spectroscopy and imaging and clearly outlines the benefits and limits of each approach, including coded aperture and imaging spectroscopy, resonant and filter-based systems, and integrated design strategies to improve image resolution, depth of field, and field of view. Optical Imaging and Spectroscopy is an indispensable textbook for advanced undergraduate and graduate courses in optical sensor design. In addition to its direct applicability to optical system design, unique perspectives on computational sensor design presented in the text will be of interest for sensor designers in radio and millimeter wave, X-ray, and acoustic systems.

Array cameras removed the optical limitations of a single camera and paved the way for high-performance imaging via the combination of micro-cameras and computation to fuse multiple aperture images. However, existing solutions use dense arrays of cameras that require laborious calibration and lack flexibility and practicality. Inspired by the cognition function principle of the human brain, we develop an unstructured array camera system that adopts a hierarchical modular design with multiscale hybrid cameras composing different modules. Intelligent computations are designed to collaboratively operate along both intra- and intermodule pathways. This system can adaptively allocate imagery resources to dramatically reduce the hardware cost and possesses unprecedented flexibility, robustness, and versatility. Large scenes of real-world data were acquired to perform human-centric studies for the assessment of human behaviours at the individual level and crowd behaviours at the population level requiring high-resolution long-term monitoring of dynamic wide-area scenes.

Designing a “cocktail party listener” that functionally mimics the selective perception of a human auditory system has been pursued over the past decades. By exploiting acoustic metamaterials and compressive sensing, we present here a single-sensor listening device that separates simultaneous overlapping sounds from different sources. The device with a compact array of resonant metamaterials is demonstrated to distinguish three overlapping and independent sources with 96.67% correct audio recognition. Segregation of the audio signals is achieved using physical layer encoding without relying on source characteristics. This hardware approach to multichannel source separation can be applied to robust speech recognition and hearing aids and may be extended to other acoustic imaging and sensing applicatio.

High-throughput imaging is highly desirable in intelligent analysis of computer vision tasks. In conventional design, throughput is limited by the separation between physical image capture and digital post processing. Computational imaging increases throughput by mixing analog and digital processing through the image capture pipeline. Yet, recent advances of computational imaging focus on the “compressive sampling”, this precludes the wide applications in practical tasks. This paper presents a systematic analysis of the next step for computational imaging built on snapshot compressive imaging (SCI) and semantic computer vision (SCV) tasks, which have independently emerged over the past decade as basic computational imaging platforms. SCI is a physical layer process that maximizes information capacity per sample while minimizing system size, power and cost. SCV is an abstraction layer process that analyzes image data as objects and features, rather than simple pixel maps. In current practice, SCI and SCV are independent and sequential. This concatenated pipeline results in the following problems: i ) a large amount of resources are spent on task-irrelevant computation and transmission, ii ) the sampling and design efficiency of SCI is attenuated, and iii ) the final performance of SCV is limited by the reconstruction errors of SCI. Bearing these concerns in mind, this paper takes one step further aiming to bridge the gap between SCI and SCV to take full advantage of both approaches. After reviewing the current status of SCI, we propose a novel joint framework by conducting SCV on raw measurements captured by SCI to select the region of interest, and then perform reconstruction on these regions to speed up processing time. We use our recently built SCI prototype to verify the framework. Preliminary results are presented and the prospects for a joint SCI and SCV regime are discussed. By conducting computer vision tasks in the compressed domain, we envision that a new era of snapshot compressive imaging with limited end-to-end bandwidth is coming.

Optical imaging has served as a primary method to collect information about biosystems across scales—from functionalities of tissues to morphological structures of cells and even at biomolecular levels. However, to adequately characterize a complex biosystem, an imaging system with a number of resolvable points, referred to as a space-bandwidth product (SBP), in excess of one billion is typically needed. Since a gigapixel-scale far exceeds the capacity of current optical imagers, compromises must be made to obtain either a low spatial resolution or a narrow field-of-view (FOV). The problem originates from constituent refractive optics—the larger the aperture, the more challenging the correction of lens aberrations. Therefore, it is impractical for a conventional optical imaging system to achieve an SBP over hundreds of millions. To address this unmet need, a variety of high-SBP imagers have emerged over the past decade, enabling an unprecedented resolution and FOV beyond the limit of conventional optics. We provide a comprehensive survey of high-SBP imaging techniques, exploring their underlying principles and applications in bioimaging.

Parallel processing in array cameras enables improvements in field of view, depth of field as well as color and polarization sampling. A recent study by Zhang et al. expands on this theme with the demonstration of “SpeedShot,” which uses a small camera array to improve temporal sensitivity. Here we review the context in which SpeedShot was developed and consider the roadmap for continued improvements in parallel camera systems.

Compared with conventional cameras, spectral imagers provide many more features in the spectral do- main. They have been used in various fields such as material identification, remote sensing, precision agriculture, and surveillance. Traditional imaging spectrometers use generally scanning systems. They cannot meet the demands of dynamic scenarios. This limits the practical applications for spectral imaging. Recently, with the rapid development in computational photography theory and semiconductor techniques, spectral video acquisition has become feasible. This paper aims to offer a review of the state-of-the-art spectral imaging technologies, especially those capable of capturing spectral videos. Finally, we evaluate the performances of the existing spectral acquisition systems and discuss the trends for future work.

Miniaturizing instruments for spectroscopic applications requires the designer to confront a tradeoff between instrument resolution and instrument throughput [and associated signal-to-background-ratio (SBR)]. This work demonstrates a solution to this tradeoff in sector mass spectrometry by the first application of one-dimensional (1D) spatially coded apertures, similar to those previously demonstrated in optics. This was accomplished by replacing the input slit of a simple 90° magnetic sector mass spectrometer with a specifically designed coded aperture, deriving the corresponding forward mathematical model and spectral reconstruction algorithm, and then utilizing the resulting system to measure and reconstruct the mass spectra of argon, acetone, and ethanol. We expect the application of coded apertures to sector instrument designs will lead to miniature mass spectrometers that maintain the high performance of larger instruments, enabling field detection of trace chemicals and point-of-use mass spectrometry. Graphical Abstract ᅟ

In mass spectrometer design, there has been a historic belief that there exists a fundamental trade-off between instrument size, throughput, and resolution. When miniaturizing a traditional system, performance loss in either resolution or throughput would be expected. However, in optical spectroscopy, both one-dimensional (1D) and two-dimensional (2D) aperture coding have been used for many years to break a similar trade-off. To provide a viable path to miniaturization for harsh environment field applications, we are investigating similar concepts in sector mass spectrometry. Recently, we demonstrated the viability of 1D aperture coding and here we provide a first investigation of 2D coding. In coded optical spectroscopy, 2D coding is preferred because of increased measurement diversity for improved conditioning and robustness of the result. To investigate its viability in mass spectrometry, analytes of argon, acetone, and ethanol were detected using a custom 90-degree magnetic sector mass spectrometer incorporating 2D coded apertures. We developed a mathematical forward model and reconstruction algorithm to successfully reconstruct the mass spectra from the 2D spatially coded ion positions. This 2D coding enabled a 3.5× throughput increase with minimal decrease in resolution. Several challenges were overcome in the mass spectrometer design to enable this coding, including the need for large uniform ion flux, a wide gap magnetic sector that maintains field uniformity, and a high resolution 2D detection system for ion imaging. Furthermore, micro-fabricated 2D coded apertures incorporating support structures were developed to provide a viable design that allowed ion transmission through the open elements of the code. Graphical Abstract ᅟ

Digital processing of optical coherence functions can reconstruct three-dimensional objects illuminated by incoherent light. It is shown that Fourier analysis of the mutual intensity of the field produces projections that are mathematically identical to the projections of x-ray cone-beam tomography. A lensless interferometric camera that captures planes of mutual intensity data is described and used to reconstruct an incoherently illuminated visible object in three dimensions.