Fuzzy Based Mean Filtering Techniques Using Nlm Algorithm

Abstract

In this paper, a new Fuzzy based Fast Non Local Mean algorithm is proposed to denoise Rician noise from MRI images, when an image is acquired by a camera or other imaging system, often the vision system for which it is intended is unable to use it directly. The image may be corrupted by random variations in intensity, variations in illumination, or poor contrast that must be dealt with in the early stages of vision processing. Many images contain unevenly distributed gray values. It is common to find images in which all intensity values lie within a small range, such as the image with poor contrast shown in Fig.1. Histogram equalization is a method for stretching the contrast of such images by uniformly redistributing the gray values. This step may make threshold selection approaches more effective. In general, histogram modification enhances the subjective quality of an image and is useful when the image is intended for viewing by a human observer Recently non-local means has been extended to other image processing applications such as deinterlacing, view interpolation, and depth maps regularization. The proposed method gave better result than existing Fast NLM technique with high and density Rician noise in the image and it is Fast than NLM.