Neural networks for halftoning of color images

Daniel T. Ling; Dieter Just

doi:10.1117/12.45365

1 June 1991 Neural networks for halftoning of color images

Daniel T. Ling, Dieter Just

Proceedings Volume 1452, Image Processing Algorithms and Techniques II; (1991) https://doi.org/10.1117/12.45365
Event: Electronic Imaging '91, 1991, San Jose, CA, United States

Abstract

This paper illustrates the use of Hopfield neural networks to halftone color images. We define an error function which is the weighted sum of squared errors of the Fourier components of the original and halftoned images. The weights can be chosen to match the human visual system or other input/output transfer functions. The error function is minimized by using a neural network and solving its dynamical equation iteratively. FFTs are used to perform the necessary convolutions so that the computational requirements are reasonable even for large images.

Citation Download Citation

Daniel T. Ling and Dieter Just "Neural networks for halftoning of color images", Proc. SPIE 1452, Image Processing Algorithms and Techniques II, (1 June 1991); https://doi.org/10.1117/12.45365

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