Adaptive Computational Imaging
Performance Analysis of an Adaptive, Multi-aperture Computational Imaging Architecture utilizing Micromirror array
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The traditional single aperture optical imagers areconstrained by their large form factor that makesthem bulky. This constraint restricts their utilityin many scenarios where their pervasive use wouldotherwise be beneficial. Existing architectures likeTOMBO, with multiple apertures and optimalreconstruction algorithms, gives a flat sensor butthey are not adaptive to information content in thescene. An adaptive, flat multi-aperture computationalimaging sensor architecture named PANOPTES ispresented. It utilizes the arrays of micro-mirrors tosteer the field of view of many low resolutionimaging ...
The traditional single aperture optical imagers are
constrained by their large form factor that makes
them bulky. This constraint restricts their utility
in many scenarios where their pervasive use would
otherwise be beneficial. Existing architectures like
TOMBO, with multiple apertures and optimal
reconstruction algorithms, gives a flat sensor but
they are not adaptive to information content in the
scene. An adaptive, flat multi-aperture computational
imaging sensor architecture named PANOPTES is
presented. It utilizes the arrays of micro-mirrors to
steer the field of view of many low resolution
imaging sensors. This research analyzes the
implications of scaling down the optical systems, to
short working distances (to achieve flat form
factor), on the performance of the system in terms of
its sensitivity and the spatial resolution. The
effects of using micro-mirror arrays at
the sensor pupil are investigated in terms of the
degradation in the MTF. The overall performance
improvement scheme based on adaptive and redundant
resources is proposed. A framework to achieve an all
encompassing performance metric for a multi-aperture
computational imager is presented.
constrained by their large form factor that makes
them bulky. This constraint restricts their utility
in many scenarios where their pervasive use would
otherwise be beneficial. Existing architectures like
TOMBO, with multiple apertures and optimal
reconstruction algorithms, gives a flat sensor but
they are not adaptive to information content in the
scene. An adaptive, flat multi-aperture computational
imaging sensor architecture named PANOPTES is
presented. It utilizes the arrays of micro-mirrors to
steer the field of view of many low resolution
imaging sensors. This research analyzes the
implications of scaling down the optical systems, to
short working distances (to achieve flat form
factor), on the performance of the system in terms of
its sensitivity and the spatial resolution. The
effects of using micro-mirror arrays at
the sensor pupil are investigated in terms of the
degradation in the MTF. The overall performance
improvement scheme based on adaptive and redundant
resources is proposed. A framework to achieve an all
encompassing performance metric for a multi-aperture
computational imager is presented.
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