presented by:
MS.Radha,J.V.Sangeetha,S.Suba bala sathiy

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ABSTRACT:
Image signals might be corrupted by Impulse noise in the process of signal acquisition and transmission. In this paper,an efficient VLSI implementation for removing impulse noise is presented.Our extensive experimental results show that the proposed technique preserves the edge features and obtains excellent performances in terms of quantitative evaluation and visual quality. The design requires only low computational complexity and two line memory buffers. Its hardware cost is quite low.Compared with previous VLSI implementations,our design achieves better image quality with less hardware cost. Synthesis results show that the proposed design yields a processing rate of about 167 M samples/second by using TSMC 0.18 m Technology.
I.INTRODUCTION
IN SUCH applications as printing skills, medical imaging,
scanning techniques, image segmentation, and face recognition, images are often corrupted by noise in the process of image
acquisition and transmission. Hence, an efficient denoising technique is very important for the image processing applications . Recently, many image denoising methods have been proposed to carry out the impulse noise suppression . Some of them employ the standard median filter or its modifications , to implement denoising process.However, these approaches might blur the image since bothnoisy and noise-free pixels are modified. To avoid the damage on noise-free pixels, an efficient switching strategy has been proposed in the literature .
In general, the switching median filter consists of twosteps: 1) impulse detection and 2) noise filtering. It locates thenoisy pixels with an impulse detector, and then filters themrather than the whole pixels of an image to avoid the damageon noise-free pixels. Generally, the denoising methods forimpulse noise suppression can be classified into two categories:lower-complexity techniques and higher-complexity techniques . The former uses a fixed-size local window and requires a few line buffers. Furthermore, its computational complexity is low and can be comparable to conventional median filter or its modification . The latter yields visually pleasing images by enlarging local window size adaptively, or doing iterations . In this paper, we focus only on the lower-complexity denoising techniques because of its simplicity and easy implementation with the VLSI circuit.In , Zhang and Karim proposed a new impulse detector(NID) for switching median filter. NID used the minimum absolutevalue of four convolutions which are obtained by using 1-D Laplacian operators to detect noisy pixels.A method named as differential rank impulse detector (DRID) is presented . The impulse detector of DRID is based on a comparison of signal samples within a narrow rank window by both rank and absolute value. Luo proposed a method which can efficiently remove the impulse noise (ERIN) based on simplefuzzy impulse detection technique. An alpha-trimmed mean based method (ATMBM) was presented . It used the alpha trimmedmean in impulse detection and replaced the noisy pixel value by a linear combination of its original value and the median of its local window. , a decision-based algorithm (DBA) is proposed to remove the corrupted pixel by the median or by its neighboring pixel value according the proposed decisions.For real-time embedded applications, the VLSI implementationof switching median filter for impulse noise removal is necessaryand should be considered. For customers, cost is usually the most important issue while choosing consumer electronic products. We hope to focus on low-cost denoising implementation in this paper. The cost of VLSI implementation depends mainly on the required memory and computational complexity.Hence, less memory and few operations are necessary for a low-cost denoising implementation. Based on these two factors,we propose a simple edge-preserved denoising technique (SEPD) and its VLSI implementation for removing fixed-value impulse noise. The storage space needed for SEPD is two line
buffers rather than a full frame buffer. Only simple arithmetic operations, such as addition and subtraction, are used in SEPD.
We proposed a useful impulse noise detector to detect the noisy
pixel and employ an effective design to locate the edge of it. The
experimental results demonstrate that SEPD can obtain better
performances in terms of both quantitative evaluation and visual
quality than other state-of-the-art lower-complexity impulse denoising methods. Furthermore, the VLSI implementation
of our method also outperforms previous hardware circuits in terms of quantitative evaluation, visual quality, and hardware cost.The rest of this paper is organized as follows. In Section II,the proposed SEPD is introduced. The VLSI implementation of SEPD is described briefly in Section III. In Section IV, the implementation of reduced SEPD is introduced. The implementation results and comparison are provided in Section V. Conclusionsare presented in Section VI.
II. PROPOSED SEPD
Assume that the current pixel to be denoised is located at coordinate (i,j) and denoted as pi,j ,and its luminance values before and after the denoising process are represented as f i,j and fˆi,j, respectively. If pi,j is corrupted by the fixed-value impulse noise, its luminance value will jump to be the minimum or maximum value in gray scale. Here, we adopt a 3×3 mask W centering on pi,j for image denoising. In the current W, we know that the three denoised values at coordinates(i-1,j-1),(i-1,j)and(i-1,j+1) are determined at the previous denoising process, and the six pixels at coordinates (i,j-1),(i,j),(i,j+1),(i+1,j-1),(i+1,j),,(i+1,j+1) are not denoised yet, as shown in Fig. 1. A pipelined hardware architecture is adopted in the design, so we assume that the denoisedvalue of pi,j-1 is still in the pipeline and not available. Usingthe 3×3 values in W,SEPD will determine whether pi,j is anoisy pixel or not. If positive, SEPD locates a directional edge existing in W and uses it to determine the reconstructed value fˆi,j; otherwise,fˆi,j =fi,j. SEPD is composed of three components: extreme data detector, edge-oriented noise filter and impulse arbiter. Theextreme data detector detects the minimum and maximum luminance values in W,and determines whether the luminance values of pi,j and its five neighboring pixels are equal to the extreme data. By observing the spatial correlation, the edge-oriented noise filter pinpoints a directional edge and uses it to generate the estimated value of current pixel. Finally, the impulse arbiter brings out the proper result.The three components of SEPD are described in detail in the following subsections.
A. Extreme Data Detector
The extreme data detector detects the minimum and maximum luminance values ( MINinWandMAXinW ) in those processed masks from the first one to the currentone in the image. If a pixel is corrupted by the fixed-valueimpulse noise, its luminance value will jump to be the minimumor maximum value in gray scale. If fi.j is not equal to MINinW/MAXinW, we conclude that pi,j is a noise-freepixel and the following steps for denoising pi,j are skipped. If is equal to MINinWorMAXinW ,we set φ to 1, check whether its five neighboring pixels are equal to the extreme data, and store the binary compared results into
B. Edge-Oriented Noise Filter
To locate the edge existed in the current W, a simple edge catchingtechnique which can be realized easily with VLSI circuit
is adopted. To decide the edge, we consider 12 directional differences, from D1 to D12 , as shown in Fig. 3. Only those composed of noise-free pixels are taken into account to avoid possible misdetection. If a bit in B is equal to 1, it means that the pixel related to the binary flag is suspected to be a noisy pixel. Directions passing through the suspected pixels are discarded to reduce misdetection. In each condition, at most four directions are chosen for low-cost hardware implementation. If
there appear over four directions, only four of them are chose accordingto the variation in angle. Fig. 4 shows the mapping table
between B and the chosen directions adopted in the design.If pi,j-1 ,pi,j+1, pi+1,j-1,pi+1,jand pi+1,j+1 are all suspected to be noisy pixels (B=“11111”), no edgecan be processed, so fˆi.j (the estimated value of pi,j)is equal to the weighted average of luminance values of three previously denoised pixels and calculated as In other conditionsexcept when “ B=11111” the edge filter calculates thedirectional differences of the chosen directions and locates the smallest one (Dmin) among them, as shown in Fig. 2. The smallest directional difference implies that it has the strongest spatial relation with pi,j , and probably there exists an edge in its direction. Hence, the mean of luminance values of the two pixels which possess the smallest directional difference is treated as fˆi,j . For example, if is equal to “10011,” it means that fi,j-1,fi+1,j And fi+1,j+1 are suspected to be noisy values. Therefore, D2 _D5 ,D7 and D9_ D11 discarded because they contain those suspected pixels (see Fig. 3). The four chosen directional differences are D1, D6 , D8 and D12 (see Fig. 4). Finally, is
equal to the mean of luminance values of the two pixels which possess the smallest directional difference among D1,D6, D8 and D12.
C. Impulse Arbiter
Since the value of a pixel corrupted by the fixed-value impulse noise will jump to be the minimum/maximum value in
gray scale, we can conclude that if pi,j is corrupted,f i,j is equal
to MINinW orMAXinW . However, the converse is not true.
If fi,j is equal to MINinW or MAXinW,pi,j, may be corrupted
or just in the region with the highest or lowest luminance.
In otherwords, a pixel whose value is MINinW or MAXinW
might be identified as a noisy pixel even if it is not corrupted. To overcome this drawback, we add another condition to reduce the
possibility of misdetection. If pi,j is a noise-free pixel and the current mask has high spatial correlation, f i,j should be close to fˆi,j and |f i,j _ fˆi,j|is small. That is to say,pi,j might be a noise-free pixel but the pixel value is MINinW orMAXinW if |f i,j _ fˆi,j| is small. We measure and |f i,j _ fˆi,j| and compare it with a threshold to determine whether pi,j is corrupted or not. The threshold, denoted as Ts, is a predefined value. Obviously, the threshold affects the performance of the proposed method. A more appropriate threshold can achieve a better detection result.However, it is not easy to derive an optimal threshold through analytic formulation. According to our experimental results, we
set the threshold Ts as 20. If pi,j is judged as a corrupted pixel,
the reconstructed luminance value f ˆi,jis equal to fˆi,j ; otherwise, fˆ i,j =fi,j
III. IMPLEMENTATION OF REDUCED SEPD
In SEPD, we consider 12 directional differences to decide
the proper edge. When more edges are considered, more complex
computations are required. To further reduce the cost of
implementation, we modify SEPD and propose another design,
named as reduced SEPD (RSEPD). Only three directional differences,Da, Db, and Dc as shown in Fig, are considered in
RSEPD. As demonstrated in Section V, RSEPD offers slightly
poorer image quality but requires much lower cost than SEPD.