Reduced spatiotemporal bandwidth of motion stabilized event-based imaging sensors: experimental demonstration.

This work investigates event-based sensor (EBS) imaging system's read-out bandwidth performance under linear motion, with and without hardware stabilization techniques. We implement three image stabilization methods using hardware rotation to cancel the sensor platform's linear motion and recapture lost EBS performance. We successfully demonstrated the methods, showing a bandwidth reduction of over an order of magnitude in two scenes, 10 scene variations, and five EBS velocities.

Bounds on mutual information of mixture data for classification tasks.

To quantify the optimum performance for classification tasks, the Shannon mutual information is a natural information-theoretic metric, as it is directly related to the probability of error. The data produced by many imaging systems can be modeled by mixture distributions. The mutual information between mixture data and the class label does not have an analytical expression nor any efficient computational algorithms.

Invertibility of multi-energy X-ray transform.

Purpose: The goal is to provide a sufficient condition for the invertibility of a multi-energy (ME) X-ray transform. The energy-dependent X-ray attenuation profiles can be represented by a set of coefficients using the Alvarez-Macovski (AM) method. An ME X-ray transform is a mapping from AM coefficients to noise-free energy-weighted measurements, where .

Approaching quantum-limited imaging resolution without prior knowledge of the object location.

Passive imaging receivers that demultiplex an incoherent optical field into a set of orthogonal spatial modes prior to detection can surpass canonical diffraction limits on spatial resolution. However, these mode-sorting receivers exhibit sensitivity to contextual nuisance parameters (e.g.

Visibility of Quantization Errors in Reversible JPEG2000.

Image compression systems that exploit the properties of the human visual system have been studied extensively over the past few decades. For the JPEG2000 image compression standard, all previous methods that aim to optimize perceptual quality have considered the pipeline of the standard. In this work, we propose an approach for the pipeline of the JPEG2000 standard.

Eavesdropping of display devices by measurement of polarized reflected light.

Display devices, or displays, such as those utilized extensively in cell phones, computer monitors, televisions, instrument panels, and electronic signs, are polarized light sources. Most displays are designed for direct viewing by human eyes, but polarization imaging of reflected light from a display can also provide valuable information. These indirect (reflected/scattered) photons, which are often not in direct field-of-view and mixed with photons from the ambient light, can be extracted to infer information about the content on the display devices.

Face recognition with non-greedy information-optimal adaptive compressive imaging.

Adaptive compressive measurements can offer significant system performance advantages due to online learning over non-adaptive or static compressive measurements for a variety of applications, such as image formation and target identification. However, such adaptive measurements tend to be sub-optimal due to their greedy design. Here, we propose a non-greedy adaptive compressive measurement design framework and analyze its performance for a face recognition task.

Single molecule image formation, reconstruction and processing: introduction.

The ability to image at the single molecule scale has revolutionized research in molecular biology. This feature issue presents a collection of articles that provides new insights into the fundamental limits of single molecule imaging and reports novel techniques for image formation and analysis.

Capacity of electromagnetic communication modes in a noise-limited optical system.

We present capacity bounds of an optical system that communicates using electromagnetic waves between a transmitter and a receiver. The bounds are investigated in conjunction with a rigorous theory of degrees of freedom (DOF) in the presence of noise. By taking into account the different signal-to-noise ratio (SNR) levels, an optimal number of DOF that provides the maximum capacity is defined.

Cellular imaging of deep organ using two-photon Bessel light-sheet nonlinear structured illumination microscopy.

In vivo fluorescent cellular imaging of deep internal organs is highly challenging, because the excitation needs to penetrate through strong scattering tissue and the emission signal is degraded significantly by photon diffusion induced by tissue-scattering. We report that by combining two-photon Bessel light-sheet microscopy with nonlinear structured illumination microscopy (SIM), live samples up to 600 microns wide can be imaged by light-sheet microscopy with 500 microns penetration depth, and diffused background in deep tissue light-sheet imaging can be reduced to obtain clear images at cellular resolution in depth beyond 200 microns. We demonstrate in vivo two-color imaging of pronephric glomeruli and vasculature of zebrafish kidney, whose cellular structures located at the center of the fish body are revealed in high clarity by two-color two-photon Bessel light-sheet SIM.

Information optimal compressive sensing: static measurement design.

The compressive sensing paradigm exploits the inherent sparsity/compressibility of signals to reduce the number of measurements required for reliable reconstruction/recovery. In many applications additional prior information beyond signal sparsity, such as structure in sparsity, is available, and current efforts are mainly limited to exploiting that information exclusively in the signal reconstruction problem. In this work, we describe an information-theoretic framework that incorporates the additional prior information as well as appropriate measurement constraints in the design of compressive measurements.

Space-time compressive imaging.

Compressive imaging systems typically exploit the spatial correlation of the scene to facilitate a lower dimensional measurement relative to a conventional imaging system. In natural time-varying scenes there is a high degree of temporal correlation that may also be exploited to further reduce the number of measurements. In this work we analyze space-time compressive imaging using Karhunen-Loève (KL) projections for the read-noise-limited measurement case.

Compressive imaging: hybrid measurement basis design.

The inherent redundancy in natural scenes forms the basis of compressive imaging where the number of measurements is less than the dimensionality of the scene. The compressed sensing theory has shown that a purely random measurement basis can yield good reconstructions of sparse objects with relatively few measurements. However, additional prior knowledge about object statistics that is typically available is not exploited in the design of the random basis.

Object reconstruction from adaptive compressive measurements in feature-specific imaging.

Static feature-specific imaging (SFSI), where the measurement basis remains fixed/static during the data measurement process, has been shown to be superior to conventional imaging for reconstruction tasks. Here, we describe an adaptive approach that utilizes past measurements to inform the choice of measurement basis for future measurements in an FSI system, with the goal of maximizing the reconstruction fidelity while employing the fewest measurements. An algorithm to implement this adaptive approach is developed for FSI systems, and the resulting systems are referred to as adaptive FSI (AFSI) systems.

Increased field of view through optical multiplexing.

Traditional approaches to wide field of view (FoV) imager design usually lead to overly complex optics with high optical mass and/or pan-tilt mechanisms that incur significant mechanical/weight penalties, which limit their applications, especially on mobile platforms such as unmanned aerial vehicles (UAVs).We describe a compact wide FoV imager design based on superposition imaging that employs thin film shutters and multiple beamsplitters to reduce system weight and eliminate mechanical pointing. The performance of the superposition wide FoV imager is quantified using a simulation study and is experimentally demonstrated.

Point spread function engineering for iris recognition system design.

Undersampling in the detector array degrades the performance of iris-recognition imaging systems. We find that an undersampling of 8 x 8 reduces the iris-recognition performance by nearly a factor of 4 (on CASIA iris database), as measured by the false rejection ratio (FRR) metric. We employ optical point spread function (PSF) engineering via a Zernike phase mask in conjunction with multiple subpixel shifted image measurements (frames) to mitigate the effect of undersampling.

Compressive imaging system design using task-specific information.

We present a task-specific information (TSI) based framework for designing compressive imaging (CI) systems. The task of target detection is chosen to demonstrate the performance of the optimized CI system designs relative to a conventional imager. In our optimization framework, we first select a projection basis and then find the associated optimal photon-allocation vector in the presence of a total photon-count constraint.

Task-specific information for imaging system analysis.

Imagery is often used to accomplish some computational task. In such cases there are some aspects of the imagery that are relevant to the task and other aspects that are not. In order to quantify the task-specific quality of such imagery, we introduce the concept of task-specific information (TSI).

Pseudorandom phase masks for superresolution imaging from subpixel shifting.

We present a method for overcoming the pixel-limited resolution of digital imagers. Our method combines optical point-spread function engineering with subpixel image shifting. We place an optimized pseudorandom phase mask in the aperture stop of a conventional imager and demonstrate the improved performance that can be achieved by combining multiple subpixel shifted images.

Information-based analysis of simple incoherent imaging systems.

We present an information-based analysis of three candidate imagers: a conventional lens system, a cubic phase mask system, and a random phase mask system. For source volumes comprising relatively few equal-intensity point sources we compare both the axial and lateral information content of detector intensity measurements. We include the effect of additive white Gaussian noise.