I Introduction
A digital image is a discrete two-dimensional function f(x , y), which has been quantized over its domain and range. Noise in digital image is a random, usually unwanted, fluctuation of pixel values in an image. Now a days an image is processed in variety of ways and many important decisions are made upon that image. The presence of noise in digital image restricts us to take any decision based on that image. Most of the existing technique removes noise from image but destroys the original image information. The objective of this paper is to provide an algorithm
• To reduce noise from digital image through liner scanning based on 8-connectivity of pixels.
• To evaluate and implement a technique of noise reduction without affecting the informations of the original image.
II Methodology of The Proposed Filter
This filter is based on the fact that a pixels RGB value depends on the RGB value of its neighboring 8 connected pixels and in an discrete pixel co-ordinate image the change of pixel values does not occur suddenly rather it occurs smoothly. The main technique is described below with the help of the flow chart .
III Experimental Results
For experimentation, I have selected some images and added various kinds of noise in it. Then I have implemented my proposed filters upon the images and some of the results are in the figure1,2,&3.
IV Performance Analysis
Estimating the noise level from a single image seems like an impossible task: we need to recognize whether local image variations are due to color, texture, or lighting variations from the image itself, or due to the noise.
For performance analysis I have implemented the techniques of the existing noise reduction filters and compared my filter results with the existing filters results. Here I have designed a graph with the pixels gray value to examine the deviation of the filtered image with respect to the main image and depicted the pixel gray values of the existing filters output and proposed filter output. From which we can clearly watch that the proposed filter .deviate less than the existing filters output with respect to the original image. Here I have take into account the percentage of matching pixel values in red, green and blue scale. And after analyzing a number of test cases I have observed the results of the table 1.The performance graph is depicted in the figure below
For performance analysis I have implemented the techniques of the existing noise reduction filters and compared my filter results with the existing filters results. Here I have designed a graph with the pixels gray value to examine the deviation of the filtered image with respect to the main image and depicted the pixel gray values of the existing filters output and proposed filter output. From which we can clearly watch that the proposed filter .deviate less than the existing filters output with respect to the original image. Here I have take into account the percentage of matching pixel values in red, green and blue scale. And after analyzing a number of test cases I have observed the results of the table 1.The performance graph is depicted in the figure below
VI References:
[1] R.Steinmetz and K.Nahrstedt, ”Multimedia: Computing, Communications and applications”. PEARSON EDUCATION ASIA.
[2] R.C.Gonzalez and R.E.Woods , ”Digital Image Processing”[Second Edition]
[3] Azriel Rosenfeld, “Picture Processing by Computer”, New York: Academic Press, 1969
[4] Wilhelm Burger and Mark J. Burge,” Digital Image Processing: An Algorithmic
Approach Using Java. Springer.”
[5] “Digital Imaging”, http://www.dpreview.com
[6] K Kaas, “Secrsects of simple image processing”, http://www.gamedev.net
[7] Clifford Watson, “Linear filtering”, http://www.cs.washington.edu
[8] M. A. Schulze, “What are the mean and median filtering”, http://www.markschulze.net
[9] Jérôme Guinot , “ Image Filtering with GLSL-Convolution Kernels
”,http://www.ozone3d.net
[10] Noura Azzabou, Nikos Paragios, Frederic Guichard, “ Application of Particle Filtering to
Image Enhancement”
[11] Md. Al-Amin Bhuiyan and Chang Hong Liu, “Intelligent Vision System for Human-Robot Interface”
[2] R.C.Gonzalez and R.E.Woods , ”Digital Image Processing”[Second Edition]
[3] Azriel Rosenfeld, “Picture Processing by Computer”, New York: Academic Press, 1969
[4] Wilhelm Burger and Mark J. Burge,” Digital Image Processing: An Algorithmic
Approach Using Java. Springer.”
[5] “Digital Imaging”, http://www.dpreview.com
[6] K Kaas, “Secrsects of simple image processing”, http://www.gamedev.net
[7] Clifford Watson, “Linear filtering”, http://www.cs.washington.edu
[8] M. A. Schulze, “What are the mean and median filtering”, http://www.markschulze.net
[9] Jérôme Guinot , “ Image Filtering with GLSL-Convolution Kernels
”,http://www.ozone3d.net
[10] Noura Azzabou, Nikos Paragios, Frederic Guichard, “ Application of Particle Filtering to
Image Enhancement”
[11] Md. Al-Amin Bhuiyan and Chang Hong Liu, “Intelligent Vision System for Human-Robot Interface”
