173 Image Extraction by Patch Based Technique using Plow Denoising Algorithm 1 C. Sundhar, 2 D. Archana 1 Associate Professor, Department of ECE, IFET College of Engineering, Villupuram, India 2 Senior Assistant Professor, Department of CSE, IFET College of Engineering, Villupuram ABSTRACT In this paper, we use a Patch based Weiner filter that exploit patch redundancy for color image denoising. This framework uses both geometrically and photometrically similar patches to estimate the different filter parameters. These parameters can be accurately estimated directly from the input noisy color image. The performance of this proposed method will be on par or exceeding the current state of the art, both visually and quantitatively. Keywords: Patch redundancy, Geometrical, Photometrical, Clustering and Wiener Filter. 1. INTRODUCTION In recent years, images and videos have become integral parts of our lives. Applications now range from the casual documentation of events and visual communication to the more serious surveillance and medical fields. This has led to an ever-increasing demand for accurate and visually pleasing images. However, images captured by modern cameras are invariably corrupted by noise. With increasing pixel resolution but more or less the same aperture size, noise suppression has become more relevant. While advances in optics and hardware try to mitigate such undesirable effects, software-based denoising approaches are more popular as they are usually device independent and widely applicable. In the last decade, many such methods have been proposed, leading to considerable improvement in denoising performance. we studied the problem from an estimation theory perspective to quantify the fundamental limits of denoising. 2. CONTRIBUTION Image denoising is a very basic problem that is of wide interest to the image processing and computer vision communities. In this thesis, we perform a thorough statistical analysis of the image denoising problem leading to a practical denoising method that achieves near-optimal performance. A. Fundamental Limits for Image Denoising An expression for the performance limits of image denoising is derived. Considering the superior performance of patch-based methods, this casts the problem of denoising as that of estimating the unknown noise-free patch intensities. At each image location. A lower bound on the mean squared error (MSE) of the estimate is then formulated in a Bayesian Cram´er-Rao bound framework. B. Estimation of Denoising Bounds This deals with the estimation of the bounds from a given image. For this work, let us assume the noise to be WGN. It estimates the bounds by independently estimating the different parameters of the denoising bounds. This first presents methods of estimating the parameters assuming the noise-free image to be available. These methods are then generalized to account for the presence of noise in the input image. Through experiments, It shows that the estimated. Parameters are accurate enough to obtain an estimate of the performance limits for denoising any given noisy image. International Journal of Research in Electronics & Communication Technology Volume 1, Issue 2, October-December, 2013, pp. 173-179, © IASTER 2013 www.iaster.com, ISSN Online: 2347-6109, Print: 2348-0017