International Journal of Computer Applications (0975 – 8887) Volume 50– No.23, July 2012 1 An Enhanced Filtering Approach for High Density Salt and Pepper Noise to Restore Image with the Aid of Robust Estimator Golam Moktader Daiyan Department of Computer Science and Engineering, Islamic University of Technology, Boardbazar, Bangladesh. M. A. Mottalib Phd,Department of Computer Science and Engineering, Islamic University of Technology, Boardbazar, Bangladesh. Muhammad Mizanur Rahman Department of Computer Science and Engineering, Islamic University of Technology, Boardbazar, Bangladesh. ABSTRACT A new adaptive switching-based median filtering scheme for restoration of images that are highly corrupted by salt and pepper noise is proposed. The function of the algorithm detects the corrupted pixels first since the salt and pepper noise only affect pixels value in the image. The probable value of the noise central pixel is predicted based on noise level. Initially the algorithm adopts adaptive property for expanding the filtering window pixel by pixel until 7×7 mask. But when all the elements in 7×7 window are noise pixels the algorithm define probable pixel value through noise free last processed pixel or creating a filtering window with a big dimension and search for a pixel value which is more frequent. Then Robust Estimator Algorithm is applied to the proposed filter to remove discontinuity of pixel intensity and smooth the image. The algorithm is mainly implemented focussing on the removal of high-density salt and pepper noise in images. Extensive simulation and visual quality of image shows that it can provide high quality restored images. Keywords: Adaptive Switching Median filter; Robust Estimator; Salt and Pepper Noise. 1. INTRODUCTION Various of types of noise introduces in digital images. For example, during image acquisition, light levels and faulty sensors are the major factors affecting the amount of noise in the resulting images and electronic transmission of image data can also introduce noise due to interference in the channel used in the transmission [1]. Images are corrupted with noise modeled with a Gaussian, salt and pepper distribution, Rayleigh or an erlang and speckle noise. Different filtering may remove different types of noises, e.g., an averaging filtering is useful for removing grain noise from a photograph. So, selecting the appropriate method plays a major role in getting the desired image. Many image processing application uses linear filters for systematic theory of design and analysis [2]. If images are corrupted by Additive Gaussian Noise (AGWN) linear filters show very good performance [3]. But linear filters cannot cope with nonlinearities of the image formation model. Furthermore, human vision is very sensitive to high-frequency information. Image edge and image details (e.g. corners and lines) have high frequency content [4]. Most of the digital images require low- pass filtering. Low pass filtering tends to blur edges and destroy lines edges and other fine details. These reasons have led researchers to use nonlinear filtering techniques for image processing. An important non linear filter that will preserve the edges and remove salt and pepper noise is standard median filter [5] [6] [7]. Median filters replace every pixel by its median value from its neighborhood and often removes desirable details in the image. Many non linear filters have been proposed for removing salt and pepper noise from the images. Standard „Median shows good denosing capability at low density noise. When noise level is over 50% it could not preserve edge details of the original image. Adaptive median filter is also applicable at low density noise. The common drawback of adaptive filtering technique is that the noise pixels are replace without taking into account local features when noise level is high. The major drawback of the Decision Based method [7] is that defining a robust decision is difficult. At high density the median value will be 0 or 255 which is noisy. In such case previously processed pixel is used for replacement. This repeated replacement of neighboring pixel produce streaking effect [5]. In this paper, existing and recently improved denoising algorithms are Standard Median Filter, Adaptive Median Filter [8], Noise adaptive fuzzy switching median (NAFSM) filter [9] and A New Decision Based Algorithm [10] used to evaluate the performance level of proposed algorithm. Moreover, most of modified median filters including standard median filter cannot remove noise pixels when majority of the pixels in the filtering window are affected by noise. That do not create any new value but it is prone to alter pixels undisturbed by noise which cause some artifacts like edge jitter and streaking. Most of the modified median filters are applied uniformly. This tends to modify both noise and noise free pixels [1]. Thus overall quality of image is decreased. Adaptive Switching median filters are well known for identifying noisy pixels based on threshold values and processing only noisy pixels. There are four stages in adaptive switching median filtering: noise detection, estimation of noise-free pixels, expansion of filtering window and replacement. But edges and fine details are not recovered satisfactorily, especially when the noise level is high. In order to overcome these drawbacks we proposed a two-phase algorithm. In the first phase an adaptive median filter is used to classify corrupted and uncorrupted pixels. In the second phase, robust estimation algorithm is applied to identify the discontinuity of pixels intensity and apply their robust formulation and smooth the image. The outline of this paper is as follows. Section 2 describes robust statistics based algorithm. Section 3 discusses the proposed technique to remove salt and pepper noise. Section 4 deals the illustration of proposed technique. Section 5 deals results and discussions and conclusion is presented in section 6.