Optimal Detection Methods for the Restoration of Images Degraded by Signal Dependent Noise

Kenneth E. Barner; Gonzalo R. Arce

doi:10.1117/12.970024

1 November 1989 Optimal Detection Methods for the Restoration of Images Degraded by Signal Dependent Noise

Kenneth E. Barner, Gonzalo R. Arce

Proceedings Volume 1199, Visual Communications and Image Processing IV; (1989) https://doi.org/10.1117/12.970024
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

Abstract

The restoration of images degraded by signal dependent noise has traditionally been approached from an estimation framework. These techniques, however, are heavily dependent on a complete and accurate statistical representation of an image field and tend to blur regions where this representation is inaccurate. In this paper we introduce restoration techniques based on optimal vector detection methods. The introduced restoration methods are derived from a Bayesian framework and reduce to a M-ary Hypothesis Test among a representative set, or codebook, of vectors. These techniques remove the signal dependent noise while retaining the structure required for accurate image representation.

Citation Download Citation

Kenneth E. Barner and Gonzalo R. Arce "Optimal Detection Methods for the Restoration of Images Degraded by Signal Dependent Noise", Proc. SPIE 1199, Visual Communications and Image Processing IV, (1 November 1989); https://doi.org/10.1117/12.970024

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available