National Conference on Innovative Paradigms in Engineering & Technology (NCIPET-2012) Proceedings published by International Journal of Computer Applications® (IJCA) 1 Mutation based Bacterial Foraging Technique cascaded with Wiener Filter To Remove The Speckle Noise of Face Images Kanchan Lata Kashyap M. tech(P) DIMAT Raipur Sanjivani Shantaiya Reader (CSE Deptt.) DIMAT Raipur ABSTRACT This paper presents a new approach for the removal of noise from the face images.The approach involves removal of noise from the image by cascading the Mutation based bacteria foraging technique with wiener filter.In reality the noises that may embed into an image document will affect the performance of face recognition algorithms. Noises are of two type additive and multiplicative noise. Speckle noise is multiplicative noise, so it’s difficult to remove the multiplicative noise as compared to additive noise. Face images will be tested from database in noisy environment of speckle noise. The proposed method uses Wiener Filter and Mutation based bacteria Foraging technique(MBFO) has to be used for the removal of speckle noise . General Terms Data reduction, pre-processing, face recognition Keywords Wiener Filter , median filter,speckle noise. 1. INTRODUCTION An image is often corrupted by noise since its acquisition or transmission. The goal of de-noising is to remove the noise while retaining as much as possible the important signal features of an image. A vast literature has emerged recently on signal de-noising using nonlinear techniques, in the setting of additive white Gaussian noise. The image analysis process can be broken into three primary stages which are pre-processing, data reduction, and features analysis. Removal of noise from an image is the one of the important tasks in image processing. Depending on nature of the noise, such as additive or multiplicative noise, there are several approaches for removal of noise from an image [1]-[2]. 2. MATHEMATICAL FORMULATION OF NOISE Mathematically the image noise can be represented with the help of the equations given below: V(x, y) = g[u(x, y)] + ŋ(x, y)……………… (1) g[u(x,y)]= .(2) Ŋ(x, y)=f [g(u(x, y))] ŋ1(x, y) + ŋ2(x, y)… (3) Here u(x, y) represents the objects (means the original image) and v(x, y) is the observed image. Here h (x, y; x’, y’) represents the impulse response of the image acquiring process. The term ŋ(x, y) represents the additive noise which has an image dependent random components f [g(w)] ŋ1 and an image independent random component ŋ2. A different type of noise in the coherent imaging of objects is called speckle noise. Mathematically Speckle noise can be formulated as V(x, y) = u(x, y)s(x, y) + ŋ(x, y) ……(4) Where the speckle noise intensity is given by s(x, y) and ŋ(x, y) is a white Gaussian noise [1]-[3]. The main objective of image-de-noising techniques is to remove such noises while retaining as much as possible the important signal features. One of its main shortcomings is the poor quality of images, which are affected by speckle noise. The existence of speckle is unattractive since it disgraces image quality and affects the tasks of individual interpretation and diagnosis. An appropriate method for speckle reduction is one which enhances the signal-to-noise ratio while conserving the edges and lines in the image. 3. REVIEW OF SPECKLE FILTERS which enhances the signal to noise ratio while preserving the edges and lines in the image. To address the multiplicative nature of speckle noise, Jain developed a homomorphic approach, which is obtained by taking the logarithm of an image, translates the multiplicative noise into additive noise, and consequently applies the Wiener filtering. Recently many techniques have been purposed to reduce the speckle noise using wavelet transform as a multi-resolution image processing tool. Speckle noise is a high-frequency component of the image and appears in wavelet coefficients. One of the widespread method which is mainly exploited for speckle reduction is the wavelet shrinkage method. A comparative study between wavelet coefficient shrinkage filter and several standard speckle filters that are being largely used for speckle noise suppression which shows that the wavelet- based approach is deployed among the best for speckle removal[7][8]. 4. SPECKLE FILTERING In speckle filtering a kernel is being moved over each pixel in the image and applying some mathematical calculation by using these pixel values under the kernel and replaced the central pixel with calculated value. The kernel is moved along the image only one pixel at a time until the whole image covered. By applying these filters smoothing effect is achieved and speckle noise has been reduced to certain extent [9]. 4.1 Median filter [3]: The best known order-statistics filter is the median filter in image processing. The median filter is also the simpler technique and it also removes the speckle noise from an image and also removes pulse or spike noise[1]-[3].