Adaptive regularization in image restoration by unsupervised learning

Hau-San Wong; Ling Guan

doi:10.1117/1.482639

1 January 1998 Adaptive regularization in image restoration by unsupervised learning

Hau-San Wong, Ling Guan

Author Affiliations +

Journal of Electronic Imaging, Vol. 7, Issue 1, (January 1998). https://doi.org/10.1117/1.482639

We present a neural network based image restoration approach which makes use of multiple regularization parameters within the same image to achieve adaptive regularization. We derived an unsupervised learning scheme that estimates the appropriate parameter value for each pixel site based on information provided by the dynamics of the neural network. This scheme is based on the principle of adopting small regularization parameters for the highly textured regions to emphasize the details, while using large regularization parameters for the smooth regions to suppress the more visible noise and ringing in those regions. A secondary parameter update process is incorporated after the primary image gray values update process, to determine the appropriate local parameter at each image pixel. The variables required in the adaptation equations of the auxiliary neuron arise as byproducts of computational results of the primary neuron. As a result, the determination of the regularization parameter only involves local computation and no explicit evaluation of the underlying cost function is required. The current algorithm was applied to a number of real world images. The results correspond closely to our original expectation in that the algorithm automatically adopts small regularization parameters for the highly textured regions while maintaining high parameters for smooth regions, thus resulting in an overall pleasing appearance for the restored images.

Citation Download Citation

Hau-San Wong and Ling Guan "Adaptive regularization in image restoration by unsupervised learning," Journal of Electronic Imaging 7(1), (1 January 1998). https://doi.org/10.1117/1.482639

Published: 1 January 1998

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