A neural implementation of interpolation with a family of kernels

Author

Candocia, Frank M. ; Principe, Jose C.

Author_Institution

Comput. Neuroeng. Lab., Florida Univ., Gainesville, FL, USA

Volume

3

fYear

1997

fDate

9-12 Jun 1997

Firstpage

1506

Abstract

A paradigm for interpolating images based on a family of kernels is presented. Each kernel is “tuned” to specific image characteristics and contains the information responsible for the local creation of missing detail. This interpolation process (1) exploits the correlation that exists in the local structure of images via a self-organizing feature map (SOFM) and (2) establishes an optimal set of linear associative memories (LAMs) from the homologous neighborhoods of a set of low and high resolution image counterparts. Each LAM creates members of the family of interpolation kernels. We compare the performance of this technique with the commonly used bilinear and spline interpolation methods and demonstrate its ability to generalize well

Keywords

content-addressable storage; image sampling; interpolation; self-organising feature maps; bilinear interpolation methods; image characteristics; interpolation kernels; linear associative memories; missing detail; neural implementation; self-organizing feature map; spline interpolation methods; Associative memory; Filters; Frequency; Image reconstruction; Image sampling; Interpolation; Kernel; Laboratories; Neural engineering; Sampling methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.614020

Filename

614020