2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA 2015) March 11-12, 2015
A New Hybrid Algorithm for Speckle Noise Reduction of SAR Images Based on Mean-Median Filter and SRAD method Masume Rahimi
Mehran Yazdi
School of Electrical and Computer Engineering Shiraz University Shiraz, Iran
[email protected]
School of Electrical and Computer Engineering Shiraz University Shiraz, Iran
[email protected]
Abstract— One of the inherent characteristics of radar images is the presence of speckle noise. Speckle appears as a grainy texture in the image and highly reduces the image quality. Therefore, it is desirable to reduce speckle, prior to any image interpretation. With regard to the importance of synthetic aperture radar (SAR) images, a lot of efforts have already been made to remove speckle noise from radar images, and accordingly famous filters have been introduced, each with their special advantages and disadvantages. In this paper, we examine five methods like the ones in the field of space and frequency domain. we will compare five different approaches: Wavelet Thresholding methods, anisotropic diffusion and speckle reducing anisotropic diffusion, also we suggest a method for reducing speckle of synthetic aperture radar images which is in fact a combination of hybrid mean-median filter and the method of speckle reducing anisotropic diffusion. The results indicate that the performance of our proposed method, based on criteria such as PSNR, improving SNR, standard protect the edge ( ), in almost all cases is better the other compared methods; and it also offers more desirable results from the point of visual quality. Keywords: Despeckling, Detail preservation, SAR images, Speckle noise.
I. INTRODUCTION Synthetic aperture radar (SAR) remote sensing [1] offers a number of advantages over optical remote sensing, mainly the all-day, all-weather acquisition capability. High-quality SAR images are widely used both in military and civilian fields, marine monitoring and research on forest mapping and fire prevention [2], etc. Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is a granular noise that degrades the quality of SAR information content. Image interpretation, recognition and terrain classification processes become very difficult due to the presence of speckle noise [3]. A preliminary processing of real-valued detected SAR images aimed at speckle reduction. Such a preprocessing, however, should be carefully designed
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to avoid spoiling useful information, such as local mean of backscatter, point targets, linear features and textures. There are two major categories of techniques for speckle reduction of SAR images, which are multi-look processing and image domain filtering techniques. In the last several decades, researchers have taken great efforts to eliminate speckle noise and proposed some methods. Lee suggested that speckle filters should base on statistical property of image, and used linear model to approximate multiple model of SAR image, gained Lee filter [4], Kuan filter [5], Frost filter [6]. More recently, there has been active research on denoising with the wavelet transform; Donoho and Johnstone [7] were the first to propose a novel approach for noise reduction by employing threshold in the wavelet domain. Various wavelet bases and threshold techniques have been used in reduction of speckle noise in SAR images [8,9]. Recently several works [10,11,12,13] have been conducted to improve the quality of anisotropic diffusion in speckle reduction. The most representative one among them is SRAD method proposed by Yu and Acton [8]. So in this paper, we outline a partial differential equation (PDE) approach to anisotropic diffusion (AD), and speckle removal that we call speckle reducing anisotropic diffusion (SRAD). These filters have the combined property of edge preservation and noise removal. Each filter is different from the other in terms of partial differentiation equation used. The aim of this paper is to focus on presenting a new speckle reduction algorithm based on the hybrid mean-median filter and SRAD method. The proposed filter is used for the suppression of noise at moderate and high standard deviation and also smoothing image to make it more clearer and sharpener while preserving its edge. The experimental results reveal that the proposed filter outperforms the existing methods when tested on a set of images at different noise levels. The paper is organized as follows: Section II describes the mathematical model of speckle noise. Section III presents a comprehensive review of the adaptive speckle filters, anisotropic diffusion and speckle reducing anisotropic
2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA 2015) March 11-12, 2015 diffusion methods and wavelet with hard and soft threshold. Section IV introduces proposed filtering technique which is the method used for reducing the speckle noise from SAR image. Section V presents quality evaluation metrics used for evaluating the quality of speckle reduction technique. In section VI, presents the experimental results on SAR image and the conclusion is given in section VII. II. SPECKLE NOISE IN SAR IMAGES Speckle is not a noise in an image but noise-like variation in contrast. It arises from random variations in the strength of the backscattered waves from objects and is seen mostly in RADAR imaging. Speckle noise is defined as multiplicative noise, having a granular pattern [14]. It is the inherent property of SAR image. In the case of fully-developed speckle, the SAR image can be expressed as I(x,y)=f(x,y)*n(x,y)
(1)
Where ( , ) is the intensity of SAR image, ( , ) is the unobservable original image before speckle noise contamination and ( , ) is the multiplicative noise with unit mean. Random process ( , ) and ( , ) are independent of each other. In order to make noise-suppressed processing convenient, the logarithmic transform is applied to both sides of (1); in other words, multiplicative noise is translated to additive noise, as (2) shows: Log(I(x,y))=log(f(x,y))+log(n(x,y))
(2)
III. REVIEW OF THE EXISTENT METHODS
Fig.1. Hard Thresholding
Fig.2. Soft Thresholding
C. Anisotropic diffusion Anisotropic diffusion was introduced by Perona and Malik (PM), which since then constituted a common and useful tool for image enhancement [16]. In anisotropic diffusion the main motto is to encourage smoothening with in the region in preference to the smoothening across the edges. =
( (|∇ | ). ∇ )
(5)
( , 0) =
Where ∇ is the gradient operator, div is the divergence operator, | | denotes the magnitude, ( ) the diffusion coefficient, and the initial image. They suggested two diffusion coefficients ( )=
; ( > 0) (6)
And ( ) = exp −
(7)
A. Wavelet Thresholding Wavelet Thresholding [15], is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. It removes noise by killing coefficients that are insignificant relative to some threshold, and turns out to be simple and effective. Hard and soft Thresholding with threshold, are defined as follows: The hard Thresholding operator is defined as:
η (Y, λ) =
Y |Y| > λ 0 |Y| ≤ λ
(3)
Hard Thresholding is “keep or kill” procedure and is more intuitively appealing and also it introduces artifacts in the recovered images. The soft Thresholding operator on the other hand is defined as: η (Y, λ) =
sign(Y). (|Y| − λ) |Y| > λ (4) 0 |Y| ≤ λ
It is also found to yield visually more pleasing images.
is the edge magnitude parameter. In this Where, anisotropic diffusion method, for finding edges as a step discontinuity, gradient magnitude is used. If |∇ | ≫ , then (|∇ |) → 0, an all pass filter is used; if |∇ | ≪ , then, (|∇ |) → 1, isotropic diffusion is achieved. D. SPECKLE REDUCING ANISOTROPIC DIFFUSION In [10], Yu and Acton proposed new anisotropic diffusion model to smooth speckle images where output image ( , ; ) is computed using the following differential equation: For an intensity image ( , ) over the image support Ω, the output image ( , ; 0) is given by the following PDE. ∂ ( , ; ) = ∂t ( , ; 0) =
( , ) ,
( , ; ) ∇ ( , ; ) ( , ; )
(8) ∂Ω = 0 ∂
Where, represent diffusion time, ∂Ω denotes the border of Ω, n is the outer normal to ∂Ω, their diffusion coefficient ( ) is written as:
2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA 2015) March 11-12, 2015 ( )=
1+[
( , ; )−
1 ( )]
( ) 1+
( )
(9)
Or, ( ) = exp − [
(m,n) = median(C, median (D), mean (H ,V), mean(D)) (12) ( , ; )−
( )]
( ) 1+
( )
(10)
Where is the instantaneous coefficient of variation and is given by: 1 ( , ; )=
vertical pixels. Mean of Diagonal pixels and median diagonal pixels. The filtered value is the median of the two mean values, median value, and the central pixel C:
2
|∇ |
− 1 16 1+ 1 4 ∇
∇ (11)
The function ( , ; ), combines normalized Laplacian operator and normalized gradient operator to detect edges from the speckled images. At the edges, Laplacian term shows zero crossing whereas gradient gives higher values allowing detection of edges in bright as well as dark regions. The function takes higher values at edges or high contrast regions, whereas, low values in homogeneous regions.
IV. PROPOSED METHOD The proposed filter is a combination of hybrid meanmedian filter [17] and SRAD method. Hybrid mean-median filter works on the sub windows similar to hybrid median filter. It computes the mean value of the 45° neighbors forming "X "i.e. diagonal pixels as well as the mean value of the 90° neighbors forming "+ "i.e. horizontal & vertical pixels including center pixel C, MX = mean value of 45° neighbors forming "X", M+ = mean value of 90° neighbors forming "+". In similar way it computes the median value of 45° neighbors forming "X". MEDX = median value of 45° neighbors forming "X".
Where is the filtered value. This filter removes noise and preserves edges simultaneously. After that we use the SRAD method. By this way we can remove noise at moderate and high standard deviation effectively. The basic idea of the speckle suppression filter that we propose is: Algorithm I This algorithm describes the implementation steps involved in Proposed Filter. Output: Denoised image Y. Input: Noisy Grayscale Image X. Assumption: Take window size n*n where n=5. Step 1: Cross Mask Create Cross Mask with 45° neighboring elements forming "X". Create MX=mean of cross Mask Create MedX=Median of Cross Mask. Step 2: Plus Mask Create Plus Mask with 90° neighboring elements forming "+" Create M+=Mean of Plus Mask Step 3: Main ( ) Filtered value ( ) = Median (MX, M+, Med X, C) Where is median of four values i.e. mean and median of diagonal elements, mean of horizontal & vertical elements and center pixel C. Step 4: Using SRAD method. Step 5: Denoised Image Fig.4. illustrates the algorithm of proposed filter
V. CRITERIA FOR QUANTIFYING ALGORITHM PRFORMANCE Fig.3.(a) The mean value of 90° neighbors forming "+ "(b) shows mean value of 45° neighbors forming "X".
This filter removes noise better than a hybrid median filter because it is a four-step ranking operation: data from different spatial directions are ranked separately. Four values are calculated; Mean of horizontal and
In our experiments, we are quantifying the algorithm performance in terms of edge preservation, signal to noise ratio (SNR), peak signal to noise ratio (PSNR), which can be evaluated as a function of the original, ( , ), and the denoised, ( , ), the metric used in our study can be defined as following:
2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA 2015) March 11-12, 2015 =
×
∑
= 10 log
( , ) − ( , ) (13)
, ∑ ,
( , ) × ×
= 20 log (
(14)
) (15)
Remember that in SAR imaging, we are interested in suppressing speckle noise while at the same time preserving the edges of the original image that often constitute features of interest. Thus, in addition to the above quantitative performance measures, we also considered a qualitative measure for edge preservation. The correlation measure (β) is computed using: ∑
β= ∑
(∆ − ∆ )(∆ − ∆ ) (∆ − ∆ ) ∑
preserves edges and details. The objective measures for different filters and two different densities. First for 60% and second for 80% noise densities that are illustrated in Table.I To demonstrate the behavior of these filters quality metric in terms of PSNR, β and SNR with respect to 60% density of noise on a real SAR image are shown in Fig.4, Fig.5 and Fig.6 respectively. The proposed method is performing effectively these metrics.
(16)
(∆ − ∆ )
Where ∆ , ∆ are high filtered of , using Laplacian filter. The correlation value is close to one when the edges are optimally preserved in the image.
Fig.4. Comparison of filters in terms of PSNR on SAR image
VI. EXPERIMENTAL RESULT AND DISCUSSION
0.95 wavelet hard T wavelet soft T
0.9
A. Experimental Analysis and Discussion
AD SRAD
0.85
TABLE I. QUANTITATIVE EVALUARTION FOR SIMULATED SAR IMAGE 60% SNR
PSNR
8.86
25.24
0.75 0.7 0.65 0.6 0.55 0.1
PSNR
7.32
23.71
0.2
0.3
0.4
0.5 noise
0.6
0.49
0.8
0.9
32 wavelet hard T wavelet soft T AD SRAD propose method
30 28
Noisy Image
0.7
Fig.5. Comparison of filters in terms of Beta on SAR image
80% SNR
propose method
0.8
Beta
Despeckling is carried out for SAR image using the different speckle reduction filters. We compare the filtering effect of Thresholding with hard and soft Thresholding, AD and SRAD methods and the filter used our algorithm (Table I). All of these filters reduce speckle noise of target SAR images with different standard deviation. Simulations are carried out in MATLAB. The performances of different Despeckling filters in terms of SNR, PSNR and β are compared in Table I.
0.46
Hard Thresholding Soft Thresholding AD SARD Proposed method
19.81
37.25
0.63
18.88
36.03
0.59
17.72
34.99
0.62
16.56
33.77
0.57
19.36 23.80 24.94
39.29 40.75 42.18
0.76 0.77 0.78
19.33 21.38 23.47
35.55 39.19 41.25
0.73 0.74 0.77
SNR
26 24 22 20
The performances of metrics show that among different speckle reduction filters listed in Table.I, the proposed filter is a combination of hybrid Mean-Median filter and SRAD method, which removes substantial amount of noise and also
18 16 0.1
0.2
0.3
0.4
0.5 noise
0.6
0.7
0.8
0.9
Fig.6. Comparison of filters in terms of SNR on SAR image
2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA 2015) March 11-12, 2015 The visual comparison of different filters is also done on a real synthetic image by the addition of speckle noise at variance of 0.8 as shown in Fig.7. The SAR image we use is the image of Union Station in Washangton D.C. and the size of the image is 400x600 pixels, It is the Ku-band (15 GHz) synthetic aperture radar carried by the Sandia Twin Otter aircraft. Fig.7 is the original SAR image is downloaded from Sandia national Laboratories. The results demonstrate that the proposed filter achieve better edge preservation as well as noise suppression.
In this paper, we propose a new method, combining the hybrid mean-median filter and SRAD method. We contrasted our method with four different methods in experiment. The result shows the proposed algorithm has more greatly improvement in reducing speckle noise especially at moderate and high density noise, smoothing and preserving edges of SAR image.
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(g) Fig.7. Visual results for SAR image (a) original image (b) Noisy image at 0.8 variance (c) Hard Thresholding (d) Soft Thresholding (e) AD (f) SRAD (g) Proposed method
VII. CONCLUSION
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