Nonlinear image restoration using a radial basis function network

Authors:
Keiji Icho;Youji Iiguni;Hajime Maeda
Affiliations:
The Research & Development Department, Matsushita Electric Industrial Co., Ltd., Osaka, Japan;Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Osaka, Japan;Department of Communications Engineering, Graduale School of Engineering, Osaka University, Osaka, Japan
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2004

Citing 8
Cited 0

A Gaussian potential function network with hierarchically self-organizing learning

Neural Networks
Universal approximation using radial-basis-function networks

Neural Computation
Radial basis function and related models: an overview

Signal Processing
Digital Image Restoration

Digital Image Restoration
Neural Networks: A Comprehensive Foundation (3rd Edition)

Neural Networks: A Comprehensive Foundation (3rd Edition)
A regularization approach to joint blur identification and image restoration

IEEE Transactions on Image Processing
RBFN restoration of nonlinearly degraded images

IEEE Transactions on Image Processing
On the efficiency of the orthogonal least squares training method for radial basis function networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a nonlinear image restoration method that uses the generalized radial basis function network (GRBFN) and a regularization method. The GRBFN is used to estimate the nonlinear blurring function. The regularization method is used to recover the original image from the nonlinearly degraded image. We alternately use the two estimation methods to restore the original image from the degraded image. Since the GRBFN approximates the nonlinear blurring function itself, the existence of the inverse of the blurring process does not need to be assured. A method of adjusting the regularization parameter according to image characteristics is also presented for improving restoration performance.