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Tajinder Kaur1, Rajiv Mahajan2, Amanpreet Singh3, and Anu Bala4

1Assistant Professor, Department of Information and Technology, Sant Baba Bhag Singh Institute of Engineering & Technology, Padhiana 2Professor, Department of Computer Science& Engineering, Global Institute of Emerging & Management Technology, Amritsar

3Associate Professor, Department of Applied Sciences, Institute of Engineering & Technology, Bhaddal 4Assistant Professor, Department of Computer Science& Engineering, Sant Baba Bhag Singh Institute of Engineering &Technology, Padhiana

Abstract

Image denoising has remained a primary problem in the field of image processing. This paper provides the comparative analysis of SRAD, median filter & wavelet, multiscale ridgelet transform. There are many technique used for denoising. Anisotropic, wavelet & multiscale ridge let is being widely used in reducing the speckle noise of medical image. The performance measure of image denoising in term of PSNR.

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Keyword: Image Denoising, Multiscale Ridge let, Transform Domain

1. Introduction

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2. Classification of Denoising Technique

There are two basic methods for image denoising one is Spatial Filtering and another is transform domain method discuss below.

2.1 Spatial filter:

Spatial filters are direct and high speed processing tools of images. This is the traditional way to remove the noise from the digital images to employ the spatial filters. We can use spatial filters of different kinds to remove different kinds of noise. Spatial filter further divided into two parts:

2.1.1. Linear filter 2.1.2 Non linear filter

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Er.Tajinder Kaur, Dr Rajiv Mahajan and Dr. Amanpreet Singh and Er.Anu Bala

2.1.1. Linear filter:

A filter method is linear when the output is a weighted sum of the input pixels. There are many different types of linear filter but only one type discuss in this paper below: a) Mean filter is one type of linear filter. A mean filter is the optimal linear filter for Gaussian noise in the sense ofmean square error. Linear filters too tend to blur sharp edges, destroy lines and other fine image details, and perform poorly in the presence of signal-dependent noise

2.1.2. Non Linear Filter

Non linear spatial filters operate on neighborhoods, and the mechanics of sliding a mask past an image are the same as was just outlined. Non linear filter further divided below:

a) Median filter: Median filtering is a common step in image processing. Median filter is a well-used nonlinearfilter that replaces the original gray level of a pixel by the median of the gray values of pixels in a specific neighborhood. The median filter is also called the order specific filter because it is based on statistics derived from ordering the elements of a set rather than taking the means. This filter is popular for reducing noise without blurring edges of the image [3].

Advantage of median filter: Median filter is much less sensitive than mean to extreme value. Filter is able to remove the outliers, without reducing the sharpness of the image.

b) Speckle Reducing Anisotropic Diffusion (SRAD) The anisotropic diffusion technique is an extension of conventional Lee filter to suppress the speckle while preserving the edges Anisotropic is being widely used in reducing speckle noise of medical image. A diffusion method tailored to ultrasonic and radar imaging application. SRAD is the edge sensitive diffusion for speckled image. The filter used here is a speckle reduction using anisotropic filter method (SRAD) by Yongiian Yu [4]. In this sense, the application of this extended version is applied for smoothing the medical ultrasound images in which signal-dependent, spatially correlated multiplicative noise is present[4].

2.2 Transform Domain:

Transform of signal is just another form of representing the signal. It does not change the information content present. In image processing there are so many transform but we are discussing only two transform namely Wavelet transform and multiscale ridge let transform.

2.2.1Wavelet Transform:

Wavelet means a small wave. A wave is an oscillating function of time or space and is periodic. In contrast, wavelets are localized waves. A wavelet is a wave form of limited duration that has an average value of zero. Unlike sinusoids that theoretically extend from minus to plus infinity wavelets have a beginning and an end. the wave and wavelet showing in diagram :

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Comparitive Analysis of SRAD,Median Filter and Wavelet transfer to Denoise the Medical Image

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)LJ'LIIHUHQFHEHWZHHQZDYHDQGZDYHOHWDZDYHOHWEZDYHThere are mainly two types of Wavelet Transforms- 1. Continuous Wavelet Transformation (CWT) 2. Discrete Wavelet Transformation (DWT) Wavelet based Image Denoising:Wavelets are basically mathematical functions which break up the data into

different frequency components, and then we study each component with a resolution matched to its scale [8]. Wavelets are the better technique to handle the different type of noises which is present in an image [10]. They have advantage over traditional Fourier methods in analyzing physical situations where the signals contain discontinuities & sharp spikes. The wavelet decomposition of an image is done as follows: In the first level of decomposition, the image is split into 4subbands, namely the HH, HL, LH and LL sub bands as shown in Figure 2. The HH sub band gives the diagonal details of the image; the HL sub band gives the horizontal features while the LH sub band represents the vertical structures[11][12]. The LL sub band is the low resolution residual consisting of low frequency components and it is this sub band which is further split at higher levels of decomposition [13].

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Algorithm: The basic Procedure for all thresholding method is as follows: Calculate the DWT of the image. Threshold the wavelet coefficients (Threshold may be universal or sub band adaptive) Compute the IDWT to get the denoised estimate.

2.2.2 Multiscale Ridge let Transform:

In transform domain methods, we divide the image in low pass and high pass coefficients. Multiscale ridge lets based on the ridge let transform combined with a spatial band pass filtering operation to isolate different scales. In case of multiscale ridge let transform we get the different scale of images and apply A^ trous algorithm to it [6].But in simple ridge let transform we cannot get the different scale of images.

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Algorithm: Apply the `a trous algorithm with J scales [6]. Apply the radon transform on detail sub-bands of J scales. Calculate ridge let coefficients by applying 1-D wavelet transform on radon coefficients. Get the multiscale ridge let coefficients for J scales.

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3. Experimental Result

We have implemented and tested filter & transform method on the medical image. Experiments are conducted on various test images by adding the different types of noise. The PSNR from various methods are compared in table .Images (Add Gaussian Noise in JPG images)

Table 1: Denoising Results of using filters. In terms of PSNR under Gaussian Noise of Zero Mean and 0.02 Variance.

Image No. SRAD Median 1 21.79 31.41 2 20.61 31.19 3 18.83 32.60 4 25.44 32.90 5 15.98 31.44

Graph 1:Denoising Results of using filters. In terms of PSNR under Gaussian Noise of Zero Mean and 0.02 Variance.

Table 2: Denoising Results of using transform. In terms of PSNR Under Gaussian Noise of Zero Mean and 0.02 Variance.

Image No. Wavelet Multiscale Ridge let

1 23.95 30.53

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Comparitive Analysis of SRAD,Median Filter and Wavelet transfer to Denoise the Medical Image

2 23.58 29.09

3 16.96 20.48

4 32.57 36.93 5 13.64 13.20

Graph 2: Denoising Results of using transform. In terms of PSNR Under Gaussian Noise of Zero Mean and 0.02 Variance.

Table 3: Denoising Results of using various filters. In terms of PSNR under Speckle Noise of 0.02 Variance.

Image No. SRAD Median

1 34.35 34.27

2 34.40 32.18

3 32.42 33.56

4 31.27 33.45

5 28.23 35.17

Graph 3: Denoising Results of using various filters. In terms of PSNR under Speckle Noise of 0.02 Variance.

Table 4: Denoising Results of using Transform Domain .In terms of PSNR under Speckle Noise of 0.02 variance

Image No. Wavelet Multiscale Ridge let

1 26.82 32.4

2 25.82 29.81

3 18.58 21.6

4 34.25 37.6

5 14.84 16.49

Graph 4: Denoising Results of using Transform Domain .In terms of PSNR under Speckle Noise of 0.02 variance

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4. Conclusions

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References

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Index

I

Image denoising, 226, 228

M

Multiscale ridge let, 228229

T

Transform domain, 226228