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When Does Computational Imaging Improve Performance?

Oliver S. Cossairt; Mohit Gupta; Shree K. Nayar

Title:
When Does Computational Imaging Improve Performance?
Author(s):
Cossairt, Oliver S.
Gupta, Mohit
Nayar, Shree K.
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-004-12
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
Abstract:
A number of computational imaging techniques have been introduced to improve image quality by increasing light throughput. These techniques use optical coding to measure a stronger signal level. However, the performance of these techniques is limited by the decoding step, which amplifies noise. While it is well understood that optical coding can increase performance at low light levels, little is known about the quantitative performance advantage of computational imaging in general settings. In this paper, we derive the performance bounds for various computational imaging techniques. We then discuss the implications of these bounds for several real-world scenarios (illumination conditions, scene properties and sensor noise characteristics). Our results show that computational imaging techniques provide a significant performance advantage in a surprisingly small set of real world settings. These results can be readily used by practitioners to design the most suitable imaging systems given the application at hand.
Subject(s):
Computer science
Item views:
115
Metadata:
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