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K-Means Based Image Denoising Using Bilateral Filtering and Total Variation

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K-Means Based Image Denoising Using Bilateral Filtering and Total Variation Journal of Computer Science 10 (12): 2608-2618, 2014 ISSN: 1549-3636 © 2014 D. Wiroteurairuang et al ., This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license doi:10.3844/jcssp.2014.2608.2618 Published Online 10 (12) 2014 (http://www.thescipub.com/jcs.toc) K-MEANS BASED IMAGE DENOISING USING BILATERAL FILTERING AND TOTAL VARIATION 1Danu Wiroteurairuang, 2Sanun Srisuk 3Chom Kimpan and 4Thanwa Sripramong 1The Electrical Engineering Graduate Program, Faculty of Engineering, Mahanakorn University of Technology, Thailand 2Faculty of Industrial Technology, Nakhon Panom University, Thailand 3Faculty of Engineering and Technology, Panyapiwat Institute of Management, Thailand 4Department of Computer Engineering, Faculty of Engineering, Mahanakorn University of Technology, Thailand Received 2014-08-18; Revised 2014-10-19; Accepted 2014-11-10 ABSTRACT Bilateral filter and Total variation image denoising are widely used in image denoising. In low noisy level, bilateral filtering is better than TV denoising for it reveals better SNR and sharper edges. However, in high noisy level, TV denoising outperforms bilateral filtering in terms of SNR and more details of non edges. It is very difficult to perform denoising of a very noisy image for the resulted image rarely improves its SNR comparing to the original noisy one. Even though Total variation image denoising could be used for a very noisy image, the resulted SNR still needs some improvement. In this research, the K-means-based Bilateral-TV denoising (K-BiTV) approach using pixel-wise bilateral filtering and TV denoising has been derived based on the gradient magnitude calculation of the guideline map using K-means clusters. The denoising result of K-BiTV was depended on the level of noise density and the appropriate cluster. The experimental result showed that comparing to the conventional TV denoising and bilateral filter, K-BiTV gave the higher SNRs for some images with higher level of noise density. Keywords: Image Denoising, Bilateral Filter, Total Variation, K-Means, Gradient Magnitude 1 −() − µ σ 2 1. INTRODUCTION G( x ) = e x 2/2 (1) σ2 π One of the most common topics in image processing is image denoising. Many researches about image where, u is the average noise intensity, σ is the standard denoising were concerned with Gaussian noise deviation of the noise intensity and 1 is the reduction. Gaussian noise could be interference with the σ2 π image data for it was frequently happened in the normalize term used to adjust the Gaussian noise into the malfunctioned sensors of typical cameras while ∞ σ 2 1 −()x − µ 2/2 photographing. The Gaussian noise could be generally normal standard:. e = 1 σ π ∫−∞ expressed by the following Equation 1: 2 Corresponding Author: Danu Wiroteurairuang, The Electrical Engineering Graduate Program, Faculty of Engineering, Mahanakorn University of Technology, Thailand Science Publications 2608 JCS Danu Wiroteurairuang et al . / Journal of Computer Science 10 (12): 2608.2618, 2014 The distribution of Gaussian function in Fig. 1 is The discretion of gray intensities in the image with relevant to all directions beginning from u and 68 Gaussian noise may be alleviated by using noise percentage of distribution was confined to the σ ranged filtering. Simple solutions including linear filters such as between -σ and +σ so that G(x) of this position is about median filter illustrated by Guichard and Morel (2001), Weiner filter and Gabor filter applied by Lindenbaum et 0.607 . The Gaussian noise in the image is equally σ2 π al. (1994) could remove some noise but fail to preserve distributed into dark and bright intensities of the image the edges and fine details of the image. Nonlinear pixels which preserves the brightness of the image but filtering are alternate methods and being focused by not the continuation of the gray intensity. The sigma many researchers. Nonlinear diffusion equation was indicates the amount of noise in the image. Generally, applied to denoising problem by Alvarez et al. (1992). the model for additive noise is defined by Equation 2: Weiner filter was used to restore the image distorted by systems with noisy point spread functions by Guan and g(x,y) = f ( x,y) + η (x,y) (2) Ward (1989). However; nonlinear filtering suffers from more computation time and some blurring of fine details or non sharp edges in the image. η where, (x, y) is the noise (additive zero mean Gaussian Recently, the methods used in Gaussian denoising noise) added to the image f (x, y). of the image are mostly involved with non-linear Such additive noise reduces the quality of the image. filtering. There were some research studies on non In order to measure the quality of the image, we can linear filtering methods in spatial domain including calculate for the signal interference by Equation 3: bilateral filtering (Tomasi and Manduchi, 1998), anisotropic filtering model (Perona and Malik, 1990), E = ∑η 2(x , y ) (3) total variation model (Rudin et al. , 1992), non local () x,y means algorithm (Buades et al. , 2005) and wavelet denoising (Coifman and Donoho, 1995; Donoho, 1995). And the square of noisy image summation is defined Notice that bilateral filter and total variation image by Equation 4: denoising are widely used in image denoising. In low noisy level, bilateral filtering is better than TV denoising S= ∑ gxy2 ( , ) (4) for it reveals better SNR and sharper edges. However, in ()x,y high noisy level, TV denoising outperforms bilateral filtering in terms of SNR and more details of non edges. Therefore, the Signal to Noise Ratio (SNR) of the It is very difficult to perform denoising of a very noisy noisy image is expressed by Equation 5: image since the resulted image rarely improves its SNR comparing to the original noisy one. Eventhough Total S variation image denoising can be used for a very noisy SNR = (5) E image, still the resulted SNR is needed to be improved. Fig. 1. Gaussian function Science Publications 2609 JCS Danu Wiroteurairuang et al . / Journal of Computer Science 10 (12): 2608.2618, 2014 r Therefore, in this research, the hybrid method is nearby pixels of ξ,k() x is used to normalize the derived by encompassing two genuine methods:- d convolution result as the following Equation 7: Bilateral filtering and Total variation methods together with K-means clustering algorithm in order to improve ∞ ∞ r r r the denoising output resulted from a very noisy image. kx() = cξ, xd ξ (7) d ∫ ∫ This hybrid method was tested and compared with its −∞−∞ genuine parent methods-bilateral filtering and Total Where Equation 8: variation image denoising method in order to examine the denoising improvement on SNR. The comparison r r 2 result revealed that the proposed method is the most ξ r r d, x 1 effective method in denoising comparing to bilateral and cξ, x =() 1/ 2 π − exp − (8) σ 2 d TV denoising method, which could be noticed by its SNR, which is the highest among those images with high level of noise density (SD>50). The rest of this study was organized as follows. The And Equation 9: next section begins with an introduction to the idea of rr rr rr Bilateral filtering and TV denoising based on Euler- dxdξ, = ξ −= xd ξ − x (9) Lagrange equations. The general idea of K-means clustering was also described in this section. In section 3, the proposed algorithm for K-BiTV is presented based Is the Euclidean distance between nearby pixels of r on the gradient magnitude of the K-means mapped ξ and the center pixel of the mask. clusters. Then the experimental result was presented in The range filter is the second characteristic of section 4. Finally, the research summary and discussion bilateral filter. It measures the similarity of gray intensity were presented in section 5 and 6, respectively. r r between the center pixel (x ) and the nearby pixels of ξ . 2. METHODS It could be expressed as follows Equation 10: r r∞ ∞ r rrr 2.1. Bilateral Filter = −1 ξ ξ ξ h()xkxr ∫ ∫ f sf ,d fx (10) −∞ −∞ Bilateral filtering proposed by Tomasi and Manduchi (1998) was meant to filtering noise without blurring the r r edges of the image. It encompasses the ideas of domain where, sfξ , fx is the function used for measuring and range filtering in order to selectively filter pixels with similar intensities by finding its averages intensity. r r the similarity of gray intensity between f ξ and f x . Pixels with different intensities from the center pixel were less significant (less weight) in the computation In this case, the weighted average is applied by than those pixels with similar intensities to the center assigning higher weight on pixels with similar intensity pixel. The process of noise filtering by selecting pixels to the center pixel and lower weight on those pixels that with similar intensities to the center pixel as the center could not meet the similarity condition. The function pixel is being denoised could be reference to evenly local used to make the normal standard in range filtering is averaging of gray intensities which still preserves edges. defined by Equation 11: The bilateral filter is consisted of both domain and range filtering. The domain filter is described by Equation 6: r∞ ∞ r rrr = ξ ξ ξ kxr ∫ ∫ f sf , fxd (11) r rrrr −∞−∞ − ∞ ∞ hxk= 1 f ξ cxd ξ, ξ (6) d ∫−∞ ∫ −∞ where, Equation 12: r where, h x is the output from low frequency domain 2 r r sfξ , fx r r r r r ξ =1 − 1 filtering that applied to the input image fξ, c ξ , x is s f , f x . exp (12) 2πσ 2 σ r used to measure the likeliness of the center pixel and the Science Publications 2610 JCS Danu Wiroteurairuang et al .
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