FLANN Detector Based Filtering of Images Corrupted by Impulse Noise

Abstract

We present a novel non-linear scheme for image restoration based on neuro-detector using Functional Link Artificial Neural Network (FLANN) followed by an improved spatial filter. The method is applied to images corrupted by impulse noise with varying strengths and different noise probability. The neural detector is based on the concept of training or learning by examples. When trained properly, the detector used to detect impulse noise in any image degraded by impulse noise. Hence, the method is suitable for real time image restoration applications. The simulated results obtained from the proposed scheme outperforms existing approaches are highly satisfactory and it outperforms the earlier suggested methods in terms of residual NSR in restored images.