An Approach for Image Enhancement Using Fuzzy Inference System for Noisy Image

Image enhancement is a technique to improve the quality of an image. The aim of image enhancement technique is to improve the interpretability or perception of information in images for human viewers, or to provide better input for other automated image processing techniques. This paper presents a fuzzy grayscale enhancement technique for low contrast image corrupted by Gaussian noise. The degradation of the low contrast image is mainly caused by the inadequate lighting during image capturing and thus eventually resulted in non uniform illumination in the image. The proposed method has better performance than available methods in the enhancement of noisy images and has been validated by the performance measures like PSNR,SNR,RMSE,MSE. Keywords-Fuzzy logic, Image enhancement, Gaussian Noise, PSNR, MSE, Visual quality. INTRODUCTION Image enhancement is the improvement of image quality to a better and more understandable level for feature extraction or image interpretation. In many applications of image processing, the input image has noise and thus may not show the features clearly[4]. The goal of image enhancement is to improve the image quality so that the processed image is better than the original image for a specific application[8].Image contrast enhancement techniques are of particular interest in photography, satellite imagery, medical applications and display devices[12].Image enhancement can be clustered into two groups namely spatial domain and frequency domain methods[7]. In the spatial method, image pixels are directly modified to enhance the image. In the latter method, the enhancement is conducted by modifying the frequency transform of the image. However, computing the enhancement in frequency domain is time consuming process even with fast transformation technique thus made it unsuitable for real time application[17]. Numerous contrast enhancement techniques[3] normalized the image intensities and often fail to produce satisfactory results for a broad range of non-uniform illumination image. Low contrast image is the image whose intensity levels of the pixels resides densely in a narrow range in the histogram of the image. The objects in this type of image are not clear or distinct. To improve the quality of the image and visual perception of human beings, different enhancement methods can be applied[2][19]. Some methods work in frequency domain, some works in spatial domain and some works in fuzzy domain[11]. The enhancement of noisy data, however, is a very critical process because the sharpening operation can significantly increase the noise[8]. The noise as additional component to the image, is occurs in image for many reasons. There are many types of noisy images, probably the most frequently occurring noise is Gaussian noise of almost any signal. Gaussian noise is caused by random fluctuations in the signal[5]. The Gaussian noise is Gaussian white noise with constant mean and variance. Probably the most frequently occurring noise is additive Gaussian noise. The PDF of a Gaussian random variable, z , is given by 1 √2 σ (1) Where z represents gray level, μ is the mean of average value of z , and σ is its standard deviation. There are lots of classical filters in the literature to remove noise. The classical filters are the mean filter, the median filter, unsharp masking[9]. The mean filter or the average filter helps in smoothing operations. It suppresses the noise that is smaller in size or any other small fluctuations in the image. It involves in calculating the average brightness values in some neighborhood m x n and replaces the gray level with an average value. Smoothing or averaging operation blurs the image and does not preserve the edges. These are not useful in removing noise spikes. Fuzzy filters provide promising result in image processing tasks that cope with some drawbacks of classical filters. Fuzzy filter is capable of dealing Journal of Volume 2,