V4I1-0317

© 2014, IJARCSSE All Rights Reserved Page | 778

Volume 4, Issue 1, January 2014 ISSN: 2277 128X

International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com

Image Restoration Using Various Methods and Performance Using

Various Parameters 1Er. Jyoti Rani,

Assistant Professor, Dept. Of CSE,

GZS PTU Campus, Bathinda, Punjab, India

2Er. Sarabjeet Kaur,

Student of M.Tech, Dept. Of CSE,

GZS PTU Campus, Bathinda, Punjab, India

Abstract: In this paper brief introduction of digital image processing is described. Mainly this paper is related to

image restoration, different types of noises are introduced and different methods which are used to remove noise are

described. Different parameters are also described to compare the results of different methods which are used. All the

work is done on medical images. At the end of paper reference and conclusion is also described.

Keywords: restoration,contour plot,histogram,performance parameter

I. INTRODUCTION Image processing means to deals with various actions to change an image. Digital image processing (DIP) is a part of signal

processing where processing of digital images using various types of computer algorithm. This algorithm can be modified so

that we can also change the appearance (color, size) of the digital image easily and quickly. Digital Image Processing has

numerous of applications in various studies and researches of science and technology. Some of fields that use Digital Image

Processing include finger print, medical fields, photography.

II. IMAGE RESTORATION It deals with improving the appearance of an image. The Image is corrected using different with improving the appearance

of an image. The Image is corrected using different correction methods like Median filtering, Linear Filtering, Adaptive

Filtering etc in order to restore an image to its original forms. Fig 1.1 shows a model of the image degradation. The image

degradation process can be modeled by the following equation [2]:

g(x,y)=H(x,y).f(x,y)+n(x,y) (1.1 )

Where, H (x,y) degradation function represents a convolution matrix that models the blurring that many imaging systems

introduce. For example, camera defocus, motion blur, imperfections of the lenses all can be modeled by H. The values

g(x,y), f(x,y), and ŋ(x,y) represent the observed or degraded image, the original image or input image and the additive

noise respectively.

Fig 1.1 Model of the Image Degradation

2.1 IMAGE RESTORATION TECHNIQUES

2.1.1 Median Filtering

As the name clear the median filter is stastistics method.In this method we find the median of the pixel the replace the

pixel by median of the gray levels in their neighbourhood of that pixels[2]:

f^ (x,y)=median{g(s,t)}

The median filter is used to remove the noise like sait and pepper.It has the capability with considerably less blurring

than liner smoothing filters of the similar size.In other words we can say that Median filtering is a very important and

widely used techniques of filtering and best known for its excellent noise reduction ability from the images.By the

filtering it keeps the edges while removing the noise.This makes the image not to blur as other smoothing methods.

http://www.ijarcsse.com/

Jyoti et al., International Journal of Advanced Research in Computer Science and Software Engineering 4(1),

January - 2014, pp.778-783


2.1.2 Adaptive Filtering

A adaptive filter that uses the gray and color space for removal impulsive noise in images. All processing is based on the

gray and color space. This can provide the best noise suppression results and better preserve thin lines, edges and image

details and yield better image quality compared to other filters.

2.1.3 Linear Filtering

Filtering is a technique for modifying or enhancing an image. For example, we can filter an image to emphasize certain

features or remove other features. Image processing operations implemented with filtering include smoothing,

sharpening, and edge enhancement. With the help of linear filter we can easily remove the noise from the image with the

help imfilter function.This filter can be implemented on salt and pepper and Gaussian noise.

2.1.3 Weiner Filtering

Wiener filter [2] incorporates both the degradation function and statistical characteristics of noise into the restoration

process. The method is founded on considering images and noise as random processes, and the objective is to find an

estimate f^ of the uncorrupted image f such that the mean square error between them is minimized. This error measure is

given by

e2 = E {(f- f

^)

2}

Where E {.} is the expected value of the argument. It is assumed that the noise and the images are uncorrelated; that one

or the other has zero mean; and that the gray levels in the estimate are a linear function of the levels in the degraded

image.

2.1.4 Histogram Equalization

This technique also used to restore the image. During histogram representation the image produces contract intensities

that are not well distributed. Therefore some types of adjustments have to be made on the image so that to have a better

contrast image. During histogram equalization the intensity values are distributed effectively. This helps areas on the

image with low contrast to have a better or high contrast.

Histogram equalization is implemented using probability. During histogram equalization the pixel values of the image

are listed and with their repetitive occurrence values. After they are listed the probability of pixel valued any given points

in the output image is calculated using cumulative probability distributed method. For this technique we have to use

histeq function [5].

2.1.5 Contrast-Limited Adaptive Histogram Equalization (CLAHE)

CLAHE is work on small regions in the image that is called tiles rather than the entire image. Each tile‟s contrast is

enhanced so histogram of the output region approximately matches the histogram specified by „distributed‟ parameter.

Operates on small regions in the image, called tiles, rather than the entire image. Each tile's contrast is enhanced, so that

the histogram of the output region approximately matches the histogram specified by the 'Distribution' parameter.[6] The

neighboring tiles are then combined using bilinear interpolation to eliminate artificially induced boundaries. The contrast,

especially in homogeneous areas, can be limited to avoid amplifying any noise that might be present in the image.

III. TO EVALUATE THE PERFORMANCE PARAMETER USED (1) Contour plots (2) Image histogram (3)Perception of human eye (4) Mean Square Error (5) Peak signal to noise ratio

(6) Structured content (7) Normalized absolute error (8) Normalized cross correlation (9) Max difference (10) Average

difference

Fig 1 orignal image Fig 2 salt n pepper noise

http://www.mathworks.com/access/helpdesk/help/toolbox/images/adapthisteq.html#bqkqi21


January - 2014, pp.778-783


Fig 4 median filter Fig 5 contour plot of median filter

Fig 6 histogram of median filter Fig 7 parameter graph of median filter

Fig 8. Histogram equalization filter Fig 9. Histrogram of Histogram equalization filter

.

Fig 10. Contour plot of Histogram equalization filter Fig 11. Clahe filter


January - 2014, pp.778-783


Fig 12. Histogram of clahe filter Fig 13. Contour plot of Clahe filter

Fig 14.Wiener filter Fig 15. Contour plot of wiener filter

Fig 16. Histogram of wiener filters Fig 17. Average filter

Fig 18.Contour plot of average filter Fig 19.Histogram of average filter


January - 2014, pp.778-783


Fig. 20 Linear filter Fig 21 Contour plot of Linear filter

Fig 22. Histogram of Linear filter Fig 23. Parameter graph of Linear filter

Table 1 Shows Average difference Table 2 Shows maximum difference

Table 3 Shows Mean square error Table 4 Shows Normalized absolute error

Table 5 Shows Normalized cross corelation Table 6 Shows Peak signal to noise ratio

Table 7 Shows Structure content


January - 2014, pp.778-783


IV. Conclusion

In Medical images, noise particles are a particularly delicate and very difficult task. A tradeoff between noise reduction

and the preservation of actual image features (without noise) has to be made in a way that enhances the diagnostically

relevant image content.In this paper,image restoration techniques has been implemented by using Various Parameters

like Contour plots, Histogram equalization, MSE, PSNR, max difference, avg difference, normalized cross correlation,

normalized absolute error, structure content as performance is measured.Salt n pepper noise can be better removed by

median filter.The performance of clahe and histogram filter is not better as compare to median,adaptive and linear filter.

Reference:

1 Chen G.Y,Bui T.D,Krzyzak A. “Image denoising with neighbour dependency and customized wavelet and

threshold” Pattern Recognition , 2005, pp. 115 – 124

2 Gonzalez,R.C., and Woods, R.E. “Digital Image processing ”, Prentice Hall of India cliffs,2002.

3 Jeba Akewak,” Digital image processing and image restoration”,May 2011

4 MATLAB 7.8.0 (R2009b), Help Documentation.

5 Poobal Sumathi,Ravindrang G,” the performance of fractal Image compression on Different imaging

modalitiesUsing objective quality”Jan 2011.

6 YAN-xin SHI, CHENG Yong-mei1” Adaptive Filter for Color Impulsive Removal Based on the HSI Color

Space”2011.

7 Raman Maini and Himanshu Aggarwal, “A Comprehensive Review of Image Enhancement Techniques,” Vol.2

Issue 3, March 2010 in Journal of computing.

8 Sangita S L, Dr.S.S.Kanade,”A brief review on image restoration in image processing”,IJARCCE ,vol. 2,issue 6,june

2013.

V4I1-0317

Documents

Transcript of V4I1-0317