Denoising using Self Adaptive Radial Basis Function

Azra Jeelani,M B Veena

doi:10.11591/ijeei.v7i4.948

Abstract

This paper presents an adaptive form of the Radial basis function neural network to correct the noisy image in a unified way without estimating the existing noise model in the image. Proposed method needs a single noisy image to train the adaptive radial basis function network to learn the correction of the noisy image. The gaussian kernel function is applied to reconstruct the local disturbance appeared because of the noise. The proposed adaptiveness in the radial basis function network is compared with the fixed form of spreadness and the center value of kernel function. The proposed solution can correct the image suffered from different varieties of noises like speckle noise, Gaussian noise, salt & pepper noise separately or combination of noises. Various standard test images are considered for test purpose with different levels of noise density and performance of proposed algorithm is compared with adaptive wiener filter.

Highlights

Designing a noise model for underwater images has become a challenging area of research
For example in SAR images, along with speckle noise, there is a presence of gaussian noise as well as salt & pepper noise
The results indicate the improvement in performance for less number of centers [5] proposed the supervised learning based on the gradient descent training. [6] presented an approach of radial basis function for nonlinear mapping from Rn to R by using conditional clustering or fuzzy clustering

Summary

Introduction

Designing a noise model for underwater images has become a challenging area of research. There are several filters designed to remove noise from the underwater images. For example in SAR images, along with speckle noise, there is a presence of gaussian noise as well as salt & pepper noise. In this regard, the artificial neural network can be considered as one of the best choices. Among the various possibilities under artificial neural network, the radial basis function model is considered in this paper because of its universal approximation capability with a simplified model of architecture. The adaptive approach is applied in defining the center as well as spreadness of kernel function to make the learning better and faster

Methods

Results

Conclusion