Regularization Theory In Discrete Image Restoration

Nicolaos B. Karayiannis; Anastasios N. Venetsanopoulos

doi:10.1117/12.968935

25 October 1988 Regularization Theory In Discrete Image Restoration

Nicolaos B. Karayiannis, Anastasios N. Venetsanopoulos

Proceedings Volume 1001, Visual Communications and Image Processing '88: Third in a Series; (1988) https://doi.org/10.1117/12.968935
Event: Visual Communications and Image Processing III, 1988, Cambridge, MA, United States

Abstract

This paper presents several aspects of the application of Regularization Theory in image restoration. This is accomplished by extending the applicability of the stabilizing functional approach to 2-D ill-posed inverse problems. Image restoration is formulated as the constrained minimization of a stabilizing functional. The analytical study of this optimization problem results in a variety of regularized solutions. A relationship between these regularized solutions and optimal Wiener estimation is identified. The resulting algorithms are evaluated through experimental results.

Citation Download Citation

Nicolaos B. Karayiannis and Anastasios N. Venetsanopoulos "Regularization Theory In Discrete Image Restoration", Proc. SPIE 1001, Visual Communications and Image Processing '88: Third in a Series, (25 October 1988); https://doi.org/10.1117/12.968935

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