Analyzing Image Filtrations by Enhanced Fuzzy Logic with Multi Quality Inputs

In this paper, we describe the Image Filtration through Fuzzy Logics in four different scenarios of Image Input as 3*3,9*9, 17*17 and 25*25 division blocks and iterating fuzzy equation on it for 2 and 8 times at constant amplification factor of 10. The input image selected for analysis is PGM (Portable Gray Map), dividing input images into matrix of m*n blocks. The input image is analyzed for multiple iterations and difference in output is significantly marked for MSE and PSNR. General Terms Image Processing, Noise Reduction and Impulse Noise. Keywords Fuzzy Filter and PGM image Filtration. 1. INTRODUCTION A PGM image represents a grayscale graphic image. For most purposes, a PGM image can just be thought of an array of arbitrary integers, and all the programs in the world that think they're processing a grayscale image can easily be tricked into processing something else [1], this extra ordinary characteristics makes PGM better than other formats for analyzing Image Filtration when it comes to divide images into blocks of m*n for input. Noise can be systematically introduced into images during acquisition and/or transmission of images [2, 3]. The impulse noise has the tendency of either relatively high or relatively low, thus it could severely degrade the image quality and some loss of information details [4]. Various filtration techniques have been proposed for removing such noise in the past and are well known that linear filters could produce serious image blurring[5,6]. Therefore non linear filters are widely exploited and improved. During the past years the variety of filter classes are developed such as (1) Classical filters; (2) Fuzzy Classical Filters i.e. fuzzy logic based filters that are modification or extension of classical filters; (3) Fuzzy Filters [7,8,9]. In this paper we exploit the use of Fuzzy Filters and resemble their usage with increase in number of iterations. The paper analyzed fuzzy filter for image inputs as block of 9*9, 17*17 and 25*25 at constant amplification factor of 10 and compared with the 3*3 block input image[10]. derivative most likely is caused by noise, while a large fuzzy