89,923 research outputs found
Intensity-only optical compressive imaging using a multiply scattering material and a double phase retrieval approach
In this paper, the problem of compressive imaging is addressed using natural
randomization by means of a multiply scattering medium. To utilize the medium
in this way, its corresponding transmission matrix must be estimated. To
calibrate the imager, we use a digital micromirror device (DMD) as a simple,
cheap, and high-resolution binary intensity modulator. We propose a phase
retrieval algorithm which is well adapted to intensity-only measurements on the
camera, and to the input binary intensity patterns, both to estimate the
complex transmission matrix as well as image reconstruction. We demonstrate
promising experimental results for the proposed algorithm using the MNIST
dataset of handwritten digits as example images
Adaptive foveated single-pixel imaging with dynamic super-sampling
As an alternative to conventional multi-pixel cameras, single-pixel cameras
enable images to be recorded using a single detector that measures the
correlations between the scene and a set of patterns. However, to fully sample
a scene in this way requires at least the same number of correlation
measurements as there are pixels in the reconstructed image. Therefore
single-pixel imaging systems typically exhibit low frame-rates. To mitigate
this, a range of compressive sensing techniques have been developed which rely
on a priori knowledge of the scene to reconstruct images from an under-sampled
set of measurements. In this work we take a different approach and adopt a
strategy inspired by the foveated vision systems found in the animal kingdom -
a framework that exploits the spatio-temporal redundancy present in many
dynamic scenes. In our single-pixel imaging system a high-resolution foveal
region follows motion within the scene, but unlike a simple zoom, every frame
delivers new spatial information from across the entire field-of-view. Using
this approach we demonstrate a four-fold reduction in the time taken to record
the detail of rapidly evolving features, whilst simultaneously accumulating
detail of more slowly evolving regions over several consecutive frames. This
tiered super-sampling technique enables the reconstruction of video streams in
which both the resolution and the effective exposure-time spatially vary and
adapt dynamically in response to the evolution of the scene. The methods
described here can complement existing compressive sensing approaches and may
be applied to enhance a variety of computational imagers that rely on
sequential correlation measurements.Comment: 13 pages, 5 figure
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