Image restoration using spectrum estimation

Ki-Woon Na; Joon-Ki Paik

doi:10.1117/12.185890

16 September 1994 Image restoration using spectrum estimation

Ki-Woon Na, Joon-Ki Paik

Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.185890
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States

Abstract

A stochastic approach to image restoration is proposed by using various spectrum estimation techniques. In order to estimate the original image from the knowledge of observed image, the minimum mean square error filter or Wiener filter is known to be optimum in the sense of minimizing the mean square error. The optimality of Wiener filter, however, holds only when the power spectra of the original image and noise are given in addition to the transfer function of the imaging system. In practice, the information of the original image is generally not available. In the present paper additive noise is assumed to be white with known variance and the Wiener filter is implemented using various estimation techniques for the original spectrum. The proposed method shows significant improvement over the conventional methods, such as the Wiener filter using constant signal-to-noise power ratio, particularly for images with low signal-to-noise ratio.

Citation Download Citation

Ki-Woon Na and Joon-Ki Paik "Image restoration using spectrum estimation", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.185890

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