1 COMPARISON OF DENOISING TECHNIQUES IN MONOCHROME AND COLOUR IMAGES ,R.K.Kulkarni 1 , ,Shilpa Joshi 2 , Department of Electronics & Communication Engineering, VESIT, Chembur , Mumbai Jayashree Khanapuri 3 Department of Electronics & Communication Engineering, K.J.S.IE.I.T,Sion , Mumbai shilpa1322@yahoo.com 2 Abstract:-- Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This paper reviews the denoising algorithms, using filtering approach, and performs their comparative study. The noise model which we have used that is Gaussian noise, salt and pepper noise,. Selection of the denoising algorithm is application dependent. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. The filtering approach has been proved to be the best when the image is corrupted with salt and pepper noise.. A quantitative measure of comparison is provided by the signal to noise ratio of the image as well as mean absolute error in the image Key words:-- impulse noise, high density noise, median filter, non linear filter, Adaptive centre weighted median filter. I INTRODUCTION Digital images could be contaminated by impulse noise during acquisition and transmission. The intensity of impulse noise has the tendency of either relatively high or low. Corruption of images by impulsive noise is a frequently encountered problem in acquisition, transmission, and processing of images, therefore one of the most common signal processing tasks involves the removal of impulsive noise from signals. Preservation of image details while eliminating impulsive noise is usually not possible during the restoration process of corrupted images. However, both of them are essential in the subsequent processing stages Due to this it could severely degrade the image quality and cause some loss of image information. Keeping the image details and removing the noise from digital image is a challenging part of image processing. Various filters have been proposed for denoising in the past and it is well known that linear filters could produce serious image blurring. As a result, non-linear filters have been widely exploited due to their much improved filtering performance, in terms of noise removal and edge/details preservation. filters are good at detecting noise even at a high noise level. Their main drawback is that the noisy pixels are replaced by some median value in their vicinity without taking into account local features such as the possible presence of edges. Hence details and edges are not recoveredsatisfactorily, especially when the noise level is high. For images corrupted by Gaussian noise, least-squares methods based on edge- preserving regularization functionals have been used successfully to preserve the edges and the details in the images. These methods fail in the presence of impulse noise because the noise is heavy tailed. Moreover the restoration will alter basically all pixels in the image, including those that are not corrupted by the impulse noise. Recently, non-smooth data-fidelity terms (e.g. ` 1) have been used along with edge preserving regularization to deal with impulse noise. The outline of the paper is as follows. In Section II, we review the denoising concept. The different types of noise are described in section III .Our denoising scheme is given in Section IV. In Section IV, we demonstrate the effectiveness of our methods using various images. II Concept Of Denoising The basic idea behind this paper is the estimation of the uncorrupted image from the distorted or noisy image, and is also referred to as image “denoising”. There are various methods to help restore an image from noisy distortions. Selecting the appropriate method plays a major role in getting the desired image. The denoising methods tend to be problem specific. For example, a method that is used to denoise satellite images may not be suitable for denoising medical images. The image s(x,y) is blurred by a linear operation and noise n(x,y) is added to form the degraded image w(x,y). This is convolved with the restoration procedure W(x,y) to produce the restored image z(x,y). Figure 1.:Denoising Concept Linear operation Denoising Technique Y(x,y) S(x,y) W (x,y) Z (x,y)