Abstract

We demonstrate a novel method that employs a frame-accumulation and shaped-function technique to improve an image sensor’s detection sensitivity using probability statistics. It can obtain the unbiased estimate for the low-light-level image signal, thus upgrading the signal-to-noise ratio to a high degree. It was verified by an experiment in a sealed box. By the help of a variable light beam–shaped function saw-tooth signal in front of a camera, the low-light-level shadow image that was invisible to the camera could be revealed clearly from the frame accumulation data. The method can surpass an image sensor’s detection limitation.

© 2012 Optical Society of America

Full Article  |  PDF Article

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (4)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (15)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription