Paper Info
Title
Image interpolation using MLP neural network with phase compensation of wavelet coefficients
Abstract
When interpolating images in the wavelet domain, the main problem is how to estimate the finest detail coefficients. Wavelet coefficients across scales have an interscale dependency, and the dependency varies according to the local energy of the coefficients. This implies the possible existence of functional mappings from one scale to another scale. If we can estimate the mapping parameters from the observed coefficients, then it is possible to predict the finest detail coefficients. In this article, we use the multilayer perceptron (MLP) neural networks to learn a mapping from the coarser scale to the finer scale. When exploiting the MLP neural networks, phase uncertainty, a well-known drawback of wavelet transforms, makes it difficult for the networks to learn the interscale mapping. We solve this location ambiguity by using a phase-shifting filter. After the single-level phase compensation, a wavelet coefficient vector is assigned to one of the energy-dependent classes. Each class has its corresponding network. In the simulation results, we show that the proposed scheme outperforms the previous wavelet-domain interpolation method as well as the conventional spatial domain methods.
Year
DOI
Venue
2009
10.1007/s00521-009-0233-7
Neural Computing and Applications
Keywords
DocType
Volume
multilayer perceptroninterpolation � interscale dependencywaveletphase-shifting filter,image interpolation,wavelet coefficient vector,MLP neural network,phase compensation,mapping parameter,finer scale,finest detail coefficient,wavelet coefficient,interscale mapping,functional mapping,wavelet domain,coarser scale
Journal
18
Issue
ISSN
Citations 
8
1433-3058
1
PageRank 
References 
Authors
0.36
23
3
Name
Order
Citations
PageRank
Sang-Soo Kim1226.25
Yoo Shin Kim2427.33
Il Kyu Eom36812.58