We exploit memory effect correlations in speckles for the imaging of incoherent fluorescent sources behind scattering tissue. These correlations are often weak when imaging thick scattering tissues and complex illumination patterns, both of which greatly limit the practicality of associated techniques. In this work, we introduce a spatial light modulator between the tissue sample and the imaging sensor and capture multiple modulations of the speckle pattern. We show that, by correctly designing the modulation patterns and the associated reconstruction algorithm, the statistical correlations in the measurements can be greatly enhanced. We exploit this to demonstrate the reconstruction of mega-pixel sized fluorescent patterns behind the scattering tissue.
Holographic displays based on spatial light modulators suffer from limited ´etendue, defined as the product of the display’s size and angular range and bounded by the number of pixel units. In this work, we suggest that rather than excessively increasing the pixel count, etendue can be expanded by augmenting the display units with tilting capabilities. We built a prototype constructed of multiple binary tilt layers and demonstrated applications of the device.
Spatial light modulator (SLM) technology forms the centerpiece of digital holographic displays. However, an inherent limitation of these devices is that their ´etendue, defined as the product of the display’s eye box and field of view (FoV), is bounded by the number of pixel units. As a consequence, current SLMs are far from meeting the required FoV and eye box for the human visual system, which would require scaling the number of display units by a few orders of magnitude. Existing strategies for ´etendue-expansion rely on introducing a diffractive optical element (DOE), a fixed random phase mask whose pitch is much smaller than that of the original display, thereby spreading light over a wider angle. Displayed content is then optimized under perceptual constraints on the generated image. However, since the phase mask is fixed, the number of degrees of freedom does not increase and hence, the expansion in ´etendue necessarily comes with a loss of image quality. The trade-offs involved with such phase masks are not well understood. This paper studies the space of phase masks that can be attached to an SLM to increase its angular range. It attempts to characterize what trade-offs are involved in ´etendue-expansion, and whatever specific phase mask designs would support better holograms. We show that while pseudo random masks support wide-´etendue, they involve an inherent loss of contrast. Perhaps surprisingly, simple commonly-available phase masks like lenslet arrays provide near-optimal results that can largely outperform random masks.
Most compressive imaging architectures rely on programmable light-modulators to obtain coded linear measurements of a signal. As a consequence, the properties of the light modulator place fundamental limits on the cost, performance, practicality, and capabilities of the compressive camera. For example, the spatial resolution of the single pixel camera is limited to that of its light modulator, which is seldom greater than 4 megapixels. In this paper, we describe a novel approach to compressive imaging that avoids the use of spatial light modulator. In its place, we use novel cylindrical optics and a rotation gantry to directly sample the Radon transform of the image focused on the sensor plane. We show that the reconstruction problem is identical to sparse tomographic recovery and we can leverage the vast literature in compressive magnetic resonance imaging (MRI) to good effect.
The proposed design has many important advantages over existing compressive cameras. First, we can achieve a resolution of N × N pixels using a sensor with N photodetectors; hence, with commercially available SWIR line-detectors with 10k pixels, we can potentially achieve spatial resolutions of 100 megapixels, a capability that is unprecedented. Second, our design is scalable more gracefully across wavebands of light since we only require sensors and optics that are optimized for the wavelengths of interest; in contrast, spatial light modulators like DMDs require expensive coatings to be effective in non-visible wavebands. Third, we can exploit properties of line-detectors including electronic shutters and pixels with large aspect ratios to optimize light throughput. On the ip side, a drawback of our approach is the need for moving components in the imaging architecture.
Mixed state or hybrid state space systems are useful tools for various problems in computer vision. These
systems model complicated system dynamics as a mixture of inherently simple sub-systems, with an additional
mechanism to switch between the sub-systems. This approach of modeling using simpler systems allows for
ease in learning the parameters of the system and in solving the inference problem. In this paper, we study
the use of such mixed state space systems for problems in recognition and behavior analysis in video sequences.
We begin with a dynamical system formulation for recognition of faces from a video. This system is used to
introduce the simultaneous tracking and recognition paradigm that allows for improved performance in both
tracking and recognition. We extend this framework to design a second system for verification of vehicles across
non-overlapping views using structural and textural fingerprints for characterizing the identity of the target.
Finally, we show the use of such modeling for tracking and behavior analysis of bees from video.
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