 Open Access
 Total Downloads : 896
 Authors : Sherin N R, Mr. Riyas A N, Mr. Rajeev S K
 Paper ID : IJERTV2IS4591
 Volume & Issue : Volume 02, Issue 04 (April 2013)
 Published (First Online): 24042013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Denoising Techniques In VLSI For Impulse Noise Removal
Denoising Techniques In VLSI For Impulse Noise Removal
Sherin N R1 PG Scholar
ECE Department
Younus College of Engineering and Technology
Kerala
Mr. Riyas A N2 Asst. Professor ECE Department
Younus College of Engineering and Technology
Kerala
Mr. Rajeev S K3 Head of the Department
ECE Department
Younus College of Engineering and Technology
Kerala
Abstract During the process of signal acquisition and transmission image and video signals might be affected by impulse noises. Nowadays many image denoising methods exists for impulse noise suppression. Here an efficient VLSI denoising technique is introduced. The method is a low complexity one and therefore its hardware cost is low. Moreover it will result in better image quality than any other technique. In addition to this another approach called decisiontreebased denoising method (DTBDM) is also included.
Keywords Image denoising, impulse noise, impulse detector, pipeline architecture,VLSI.
I. INTRODUCTION
[2] or its modifications [3], [4] to implement denoising process. According to the distribution of noisy pixel values, impulse noise can be classified into two categories: fixedvalued impulse noise and random valued impulse noise. The former is also known as salt andpepper noise.There have been many methods for removing saltandpepper noise, and some of them perform very well [4][21]. The randomvalued impulse noise is more difficult to handle due to the random distribution of noisy pixel values. We only focus on removing the randomvalued impulse noise from the corrupted image in this paper.In general, the switching median filter [5] consists of two steps: 1)impulse detection and 2)noise filtering. It locates the noisy pixels with an impulse detector, and
then filters them .In addition to median filter, there are other methods used to carry out impulse noise. In [9], Wenbin Luo proposed an alphatrimmed mean based method (ATMBM). It used the alphatrimmed mean 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 differential rank impulse detector (DRID)was presented in [7]. The impulse detector of DRID is based on a comparison of signal samples within a narrow rank window by both rank and absolute value.In [17], Petrovi NI and Crnojevi proposed a method that employed genetic programming for impulse noise filter construction.
Generally, the denoising methods can be classified into two categories: lowercomplexity techniques [8]
[13] and highercomplexity techniques [14][18]. The complexity of denoising algorithms depends mainly on the local window size, memory buffer, and iteration times. The lowercomplexity techniques use a fixedsize local window, require a few line buffers and perform no iterations. Therefore, the computational complexity is low. However, the reconstructed image quality is not good enough. The highercomplexity techniques yield visually pleasing images by using high computational complexity arithmetic operations, enlarging local window size adaptively or doing iterations. The higher complexity approaches require long computational time as well as full frame buffer. Low cost is a very important consideration in purchasing consumer electronic products. To achieve the goal of low cost, less memory and easier computations are indispensable. Based on these two factors, we propose a simple edge preserved denoising technique (SEPD) and its simulation for removing fixedvalue 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 simulation results demonstrate that SEPD can obtain better performances in terms of both quantitative evaluation and visual quality. This simulation can also be obtained by using another technique called decisiontreebased denoising method(DTBDM). The decision tree is a simple but powerful form of multiple variable analysis. It can break down a complex decisionmaking process into a collection of simpler decisions, thus provide a solution which is often easier to interpret.
The rest of this paper is organized as follows. In Section II, the SEPD is introduced. The VLSI architecture of SEPD is described briefly in Section III. In Section IV, the architecture of reduced SEPD is introduced. In section V the new technique DTBDM is presented.The simulation result is provided in Section

Conclusion is presented in Section VII.

Simple Edge Preservsed Denoising (SEPD)
Consider the current pixel to be denoised which is located at coordinate , and denoted as , and its luminance values before and after the denoising process are represented as , and , , respectively. The luminance value of , will jump to the minimum or maximum value in gray scale if it is corrupted by fixed value impulse noise. Here a 3 Ã—3 mask W centering on
, is considered for image denoising. In the current W,the three values at coordinates 1, 1 ,
1, and 1, + 1 are denoised at the previous denoising process, and the six pixels at coordinates
, 1 , , , , + 1 , + 1, 1 , + 1, and
+ 1, + 1 are not denoised yet, as shown in Fig. 1. A pipelined hardware architecture is adopted in the design. Using the 3 x 3 values in W,SEPD will determine whether , s a noisy pixel or not. If positive, SEPD locates a directional edge existing in W and uses
it to determine the reconstructed value . otherwise,
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. Fig. 2 shows the pseudo code of SEPD. The three components of SEPD are described in detail in the following subsections.

Extreme Data Detector
The extreme data detector detects the minimum and maximum luminance values (MINinW and MAXinW) in those processed masks from the first one to the current one in the image. The luminance value of a pixel will jump to the minimum or maximum value in gray scale, if it is corrupted by the fixedvalue impulse noise. If , s not equal to MINinW/MAXinW, then conclude that , is a noisefree pixel and the steps for denoising are skipped. If , is equal to MINinW or MAXinW, then set p to1, check whether its five neighboring pixels are equal to the extreme data, and store the binary compared results into B , as shown in Fig. 2.

EdgeOriented Noise Filter
To locate the edge existed in the current W, a simple edge catching technique is adopted. To decide the edge, consider 12 directional differences, from 1 to 12 , as shown in Fig. 3. Here,
only noisefree pixels are taken into account to avoid 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 not considered in order to reduce misdetection. In
,
= , .
Fig. 1. 3Ã— 3 mask centered on , .
each condition, at most four directions are chosen for lowcost hardware implementation. Fig. 4 shows the mapping table between B and the chosen directions adopted in the design.
SEPD consists of three components: extreme data detector, edeoriented noise filter and impulse arbiter. The extreme data detector detects the minimum and maximum luminance values in W, and determines whether the luminance values of , and its five neighboring pixels are equal to the extreme data. By observing the spatial correlation, the edgeoriented
Fig. 2. Pseudo code of our scaling method.
If , 1, , +1, +1, 1,+1, and +1, +1are all suspected to be noisy pixels (B= 11111) no edge can be processed, so , (the estimated value of , ) is equal to the weighted average of luminance values of three previously denoised pixels and calculated as 1, 1 + 2 Ã— 1, + 1, +1 /4 . In other conditions except when B= 11111 the edge filter calculates the directional differences of the chosen directions and locates the smallest one ( ) among them, as shown
in Fig. 2. The smallest directional difference shows that
it has the strongest spatial relation with , , and probably ther 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
, .
For example, if B is equal to 10011, it means that
, 1, +1, and +1, +1are suspected to be noisy values. Therefore 2 5 , 5 and 9 11 are discarded because they contain those suspected pixels (see Fig.3). The four chosen directional differences are
1 , 6 , and 12 (see Fig. 4). Finally, , is equal to the mean of luminance values of the two pixels which possess the smallest directional difference among 1 ,
6 , 8 and 12 .

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, then we can conclude that if , is corrupted, , is equal to MINinW or MAXinW . However, the converse is not true. If , is equal to MINinW or MAXinW , , may be corrupted or just in the region with the highest or lowest luminance value. In other words, a pixel whose value is MINinW or MAXinW might be identified as a noisy pixel even if it is not corrupted. To avoid this condition, we add another condition that, if pi,j is a noisefree
pixel and the current mask has high spatial correlation,
, should be close to , and , , is small. That is, , might be a noise free pixel but the pixel value is MINinW or MAXinW if , , is small. So, in this case we measure , , and compare it with a threshold to determine whether , is corrupted or not. The threshold, denoted as , is a predefined value. Here the threshold is set to be 20. If , is judged as a
corrupted pixel, the reconstructed luminance value , is equal to , ; otherwise, , = , .


ARCHITECTURE OF SEPD
SEPD has low computational complexity and requires only two line buffers, so its cost of VLSI implementation is low. For better timing performance, we adopt the pipelined architecture which can produce an output at every clock cycle.
Fig. 3. Twelve directional differences of SEPD.
Fig. 5 shows block diagram of the 7stage pipeline architecture for SEPD. The architecture consists of five main blocks: line buffer, register bank, extreme data detector, edgeoriented noise filter and impulse arbiter. Each of them is described briefly in the following subsections.

Line Buffer
SEPD adopts a 3×3 mask, so three scanning lines are needed. If , are processed, three pixels from 1,
and +1 are considered. With the help of four crossover multiplexers (see Fig. 5), we realize three scanning lines with two line buffers. As shown in Fig. 5, Line Bufferodd and Line Buffereven are used to store the pixels at odd and even rows, respectively.
Once the denoising process for pi,j is completed, the reconstructed pixel value , generated by the arbiter is outputted and written into the line buffer storing rowi to replace fi,j. When
Fig. 4. Thirtytwo possible values of B and their corresponding directions in SEPD.

Register Bank
The register bank (RB), consisting of 9 registers, is used to store the 3×3 pixel values of the current mask. Fig. 6 shows its architecture where Reg4 keeps the luminance value , of the current pixel to be denoised. Obviously, the denoising process for , does not start until +1, +1enterers from the input device. The nine values stored in RB are then used simultaneously by subsequent extreme data detector and noise filter for denoising.
Fig. 5. Block diagram of VLSI architecture for SEPD.
Once the denoising process shifts from , to , +1, only 3 new values 1, +2, , +2 ,+1, +2 are needed to be read into RB (Reg2, Reg5 and Reg8, respectively) and other 6 pixel values are shifted to each ones proper register. At the same time, the previous value in Reg8 (the previous input value from the input device, +1, +1
) is written back to the line buffer storing 1 for subsequent denoising process. The selection signals of the four multiplexers are all set to 1 or 0 for denoising the odd or the even rows, respectively. Two examples are shown in Fig. 7 to illustrate we denoise 2 , set all four selection signals to 0, and those samples of 1 and 2 are stored in Line Bufferodd and Buffereven respectively. The samples of row3 are inputted from the input device, as shown in Fig. 7(b). The previous interconnections between the two line buffers and RB. When value in Reg8 and the denoised results generated by the arbiter are written back to Line Bufferodd and Line Buffereven, respectively. After the denoising process of 2 has been completed, Line Bufferodd is filled with the whole samples of 3 , while those denoised samples of 2 are all stored in Line Buffer even. To denoise 3 , we set all selections signals to

Thus the previous value in Reg8 and the denoised
results generated by the arbiter are written back to Line Buffereven and Line Bufferodd, respectively, as shown in Fig. 7(a). After the denoising process of 3 has been completed, Line Buffereven is now full with the whole samples of 4 , while those denoised samples of 3 are stored in Line Bufferodd.


Extreme Data Detector
Fig. 8 shows the 3stage pipeline architecture of the extreme data detector in which P represents the pipeline register and EC is the equality comparator. The 2stage minmax tree module is used to find MINinW and MAXinW. Two columns of EC units are used to determine whether the lower six pixels in W are equal to MINinW or MAXinW, respectively. W represents the eight neighboring pixels values of , in W . The six OR gates are employed to generate the binary comparison results p and 1 5 When p=0, it means , is a noisefree pixel and the following operations can be skipped.

EdgeOriented Noise Filter
Fig. 9 shows the 2stage pipeline architecture of the edgeoriented noise filter in which the unit is used to output the absolute value of difference of two inputs. Using B from the detector, the mapping module implements the table shown in Fig. 4. Four directional differences are calculated with the four units. Then the smallest one is determined by using the minimum tree unit. After that, the mean of luminance values of the two pixels which possess the smallest directional difference can be obtained. When B=11111 " the final multiplexer willoutput 1, 1 + 2 Ã— 1, + 1, +1 /4.When B 11111
the multiplexer will output the .

Impulse Arbiter
Fig. 10 shows the architecture of the impulse arbiter in which comparator CMP is used to output logic 1 if the upper input value , , is greater than the lower one . The final multiplexer is used to output , generated by the noise filter when , is corrupted, or
, when , is noisefree.


ARCHITECTURE 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 , and as shown in Fig. 11, are considered in RSEPD. As demonstrated in Section V, RSEPD offers slightly poorer image quality but requires much lower cost than SEPD. Fig. 12 shows the pseudo code of RSEPD.
The VLSI architecture of RSEPD is also composed of five main blocks. RB, line buffer and impulse arbiter of RSPED are identical to those of SEPD; however, there are some difference between the extreme detector and noise filter and those of SEPD:
Fig. 6. Architecture of register bank in SEPD.
Fig. 7. Two examples of the interconnections between two line buffers and RB.
Fig. 8. Architecture of extreme data detector in SEPD.
Fig. 9. Twostage pipeline architecture of edgeoriented noise filter.
Fig. 10. Architecture of impulse arbiter.

In the extreme data detector of RSEPD, only one neighboring pixel +1, is considered for edge preservation. As shown in Fig.13, the extreme data detector of RSEPD contains four EC units where two EC units are used to determine p and another two are used to determine b.

In the noise filter of RSEPD, three directional differences are considered. The mapping module used in SEPD becomes unnecessary and is removed. As shown in Fig. 14, we can apply three units to calculate three differences, and then determine the smallest one with the minimum tree unit.

In RSEPD, both the extreme data detector and noise filter need three clock cycles to complete their functions. They work in parallel because there exists no data dependency between them. As shown in Fig. 10, the impulse arbiter requires one clock cycle to complete its job.
Fig. 11. Three directional differences in RSEPD.
Fig. 14. Noise filter in RSEPD.


DECISIONTREEBASED DENOISING METHOD (DTBDM)
Fig. 12. Pseudo code of RSEPD method.
Fig. 13. Extreme data detector in RSEPD
The complexity in decision making process of above techniques is rectified by introducing a new technique called DecisionTreeBased Denoising method.The decision tree is a simple but powerful form of multiple variable analysis. It can break down a complex decisionmaking process into a collection of simpler decisions, thus provide a solution which is often easier to interpret. There have been several methods using decision tree to deal with saltand pepper noise [6], [20], [16][21] and some of them perform well.
Based on above basic concepts, we present a novel adaptive decisiontreebased denoising method (DTBDM) and its VLSI architecture for removing randomvalued impulse noise. To enhance the effects of removal of impulse noise, the results of reconstructed pixels are adaptively written back as a part of input data. The proposed design requires simple computations and two line memory buffers only, so its hardware cost is low.
DTBDM consists of two components: decisiontree based impulse detector and edgepreserving image filter. The detector determines whether , is a noisy pixel by using the decision tree and the correlation between pixel , and its neighboring pixels. If the result is positive, edgepreserving image filter based on directionoriented filter generates the reconstructed value. Otherwise, the value will be kept unchanged.

SIMULATION RESULTS
To verify the characteristics and performances of various denoising algorithms, a variety of simulations are carried out on a 255Ã—255 camera man test image. In the simulations, images are corrupted by impulse noise
(saltandpepper noise). The proposed VLSI architectures of SEPD, RSEPD and DTBDM were implemented by using VHDL. MODELSIM was used for simulation.
(a) noisy image (b) SEPD (c) RSEPD (d) DTBDM Fig. 15. Results of different methods in restoring corrupted image
Camera man.

CONCLUSION

In this paper, an efficient VLSI technique for ramdomvalued impulse noise removal is presented. The extensive simulation results demonstrate that our design achieves excellent performance in terms of quantitative evaluation and visual quality. For realtime applications, a 7stage pipeline architecture for SEPD and a 5stage pipeline architecture for RSEPD are also developed and simulated. As the outcome demonstrated, RSEPD outperforms other chips [11][13], [19], [20] with the lowest hardware cost. The architectures work with monochromatic images, but they can be extended for working with RGB color images and videos. Here as a part of modification a new approach is also introduced. The approach uses the decisiontreebased detector to detect the noisy pixel and employs an effective design to locate the edge. It requires only low computational complexity and two line memory buffers. Therefore, it is very suitable to be applied to many real time applications.
ACKNOWLEDGMENT
The authors would like to thank the management, and Faculty Members, of Department of Electronics and Communication Engineering, Younus College of Engineering and Technology, Kollam for many insightful discussions and the facilities extended to us for completing the task.
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