By Topic

When Does Computational Imaging Improve Performance?

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Cossairt, O. ; Dept. of Comput. Sci., Columbia Univ., New York, NY, USA ; Gupta, M. ; Nayar, S.K.

A number of computational imaging techniques are 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. Although 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 (e.g., illumination conditions, scene properties, and sensor noise characteristics). Our results show that computational imaging techniques do not provide a significant performance advantage when imaging with illumination that is brighter than typical daylight. These results can be readily used by practitioners to design the most suitable imaging systems given the application at hand.

Published in:

Image Processing, IEEE Transactions on  (Volume:22 ,  Issue: 2 )