Partitioned computing architectures for image noise reduction

Ramakrishnan Sundaram

doi:10.1117/1.1523053

1 January 2003 Partitioned computing architectures for image noise reduction

Ramakrishnan Sundaram

Optical Engineering, Vol. 42, Issue 1, (January 2003). https://doi.org/10.1117/1.1523053

Discrete thresholded neural networks of the Hopfield type have been employed to recover the regularized least-squares (LS) solution from noisy image data. The goal is to attain this solution efficiently by conserving computational and storage requirements as the dimensions of the problem grow large. This paper discusses configurations of these networks that recover the LS solution by partitioning the networks and adopting a switching operation between active and inactive partitions to optimize the objective function. Sequential and parallel update procedures on active partitions offer a means to avoid limit cycling and imposing zero-self-feedback constraints. Examples of image noise reduction and the identification of the LS solution from corrupted image data are presented.

©(2003) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Ramakrishnan Sundaram "Partitioned computing architectures for image noise reduction," Optical Engineering 42(1), (1 January 2003). https://doi.org/10.1117/1.1523053

Published: 1 January 2003

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