Adaptive foveated single-pixel imaging with dynamic supersampling

Phillips, D. B., Sun, M.-j., Taylor, J. M. , Edgar, M. P., Barnett, S. M. , Gibson, G. M. and Padgett, M. J. (2017) Adaptive foveated single-pixel imaging with dynamic supersampling. Science Advances, 3(4), e1601782. (doi:10.1126/sciadv.1601782)

Phillips, D. B., Sun, M.-j., Taylor, J. M. , Edgar, M. P., Barnett, S. M. , Gibson, G. M. and Padgett, M. J. (2017) Adaptive foveated single-pixel imaging with dynamic supersampling. Science Advances, 3(4), e1601782. (doi:10.1126/sciadv.1601782)

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Abstract

In contrast to conventional multipixel cameras, single-pixel cameras capture images using a single detector that measures the correlations between the scene and a set of patterns. However, these systems typically exhibit low frame rates, because to fully sample a scene in this way requires at least the same number of correlation measurements as the number of pixels in the reconstructed image. To mitigate this, a range of compressive sensing techniques have been developed which use a priori knowledge to reconstruct images from an undersampled measurement set. Here, we take a different approach and adopt a strategy inspired by the foveated vision found in the animal kingdom—a framework that exploits the spatiotemporal redundancy of many dynamic scenes. In our system, a high-resolution foveal region tracks motion within the scene, yet unlike a simple zoom, every frame delivers new spatial information from across the entire field of view. This strategy rapidly records the detail of quickly changing features in the scene while simultaneously accumulating detail of more slowly evolving regions over several consecutive frames. This architecture provides video streams in which both the resolution and exposure time spatially vary and adapt dynamically in response to the evolution of the scene. The degree of local frame rate enhancement is scene-dependent, but here, we demonstrate a factor of 4, thereby helping to mitigate one of the main drawbacks of single-pixel imaging techniques. The methods described here complement existing compressive sensing approaches and may be applied to enhance computational imagers that rely on sequential correlation measurements.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Gibson, Dr Graham and Phillips, Dr David and Padgett, Professor Miles and Barnett, Professor Stephen and Edgar, Dr Matthew and Sun, Dr Mingjie and Taylor, Dr Jonathan
Authors: Phillips, D. B., Sun, M.-j., Taylor, J. M., Edgar, M. P., Barnett, S. M., Gibson, G. M., and Padgett, M. J.
College/School:College of Science and Engineering > School of Engineering > Biomedical Engineering
College of Science and Engineering > School of Physics and Astronomy
Journal Name:Science Advances
Publisher:American Association for the Advancement of Science
ISSN:2375-2548
ISSN (Online):2375-2548
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in Science Advances 3(4):e1601782
Publisher Policy:Reproduced under a Creative Commons License
Data DOI:10.5525/gla.researchdata.386

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
6672319UK Quantum Technology Hub in Enhanced Quantum ImagingMiles PadgettEngineering & Physical Sciences Research Council (EPSRC)EP/M01326X/1S&E P&A - PHYSICS & ASTRONOMY