图像处理中值滤波器中英文对照外文翻译文献

中英文资料对照外文翻译

一、英文原文

A NEW CONTENT BASED MEDIAN FILTER

ABSTRACT

In this paper the hardware implementation of a contentbased median filter suitabl e for real-time impulse noise suppression is presented. The function of the proposed ci rcuitry is adaptive; it detects the existence of impulse noise in an image neighborhood and applies the median filter operator only when necessary. In this way, the blurring o f the imagein process is avoided and the integrity of edge and detail information is pre served. The proposed digital hardware structure is capable of processing gray-scale im ages of 8-bit resolution and is fully pipelined, whereas parallel processing is used to m inimize computational time. The architecturepresented was implemented in FPGA an d it can be used in industrial imaging applications, where fast processing is of the utm ost importance. The typical system clock frequency is 55 MHz.

1. INTRODUCTION

Two applications of great importance in the area of image processing are noise filtering and image enhancement [1].These tasks are an essential part of any image pro cessor,whether the final image is utilized for visual interpretation or for automatic an alysis. The aim of noise filtering is to eliminate noise and its effects on the original im age, while corrupting the image as little as possible. To this end, nonlinear techniques (like the median and, in general, order statistics filters) have been found to provide mo re satisfactory results in comparison to linear methods. Impulse noise exists in many p ractical applications and can be generated by various sources, including a number of man made phenomena, such as unprotected switches, industrial machines and car ign ition systems. Images are often corrupted by impulse noise due to a noisy sensor or ch annel transmission errors. The most common method used for impulse noise suppressi on n forgray-scale and color images is the median filter (MF) [2].The basic drawback o f the application of the MF is the blurringof the image in process. In the general case,t he filter is applied uniformly across an image, modifying pixels that arenot contamina ted by noise. In this way, the effective elimination of impulse noise is often at the exp ense of an overalldegradation of the image and blurred or distorted features[3].In this paper an intelligent hardware structure of a content based median filter (CBMF) suita ble for impulse noise suppression is presented. The function of the proposed circuit is to detect the existence of noise in the image window and apply the corresponding MF

only when necessary. The noise detection procedure is based on the content of the im age and computes the differences between the central pixel and thesurrounding pixels of a neighborhood. The main advantage of this adaptive approach is that image blurrin g is avoided and the integrity of edge and detail information are preserved[4,5]. The pro posed digital hardware structure is capable of processing gray-scale images of 8-bitres olution and performs both positive and negative impulse noise removal. The architectt ure chosen is based on a sequence of four basic functional pipelined stages, and parall el processing is used within each stage. A moving window of a 3×3 and 5×5-pixel im age neighborhood can be selected. However, the system can be easily expanded to acc ommodate windows of larger sizes. The proposed structure was implemented using fi eld programmable gate arrays (FPGA). The digital circuit was designed, compiled and successfully simulated using the MAX+PLUS II Programmable Logic Development S ystem by Altera Corporation. The EPF10K200SFC484-1 FPGA device of the FLEX1 0KE device family was utilized for the realization of the system. The typical clock fre quency is 55 MHz and the system can be used for real-time imaging applications whe re fast processing is required [6]. As an example,the time required to perform filtering of a gray-scale image of 260×244 pixels is approximately 10.6 msec.

2. ADAPTIVE FILTERING PROCEDURE

The output of a median filter at a point x of an image f depends on the values of t he image points in the neighborhood of x. This neighborhood is determined by a wind ow W that is located at point x of f including n points x1, x2, …, xn of f, with n=2k+1. The proposed adaptive content based median filter can be utilized for impulse noisesu p pression in gray-scale images. A block diagram of the adaptive filtering procedure is depicted in Fig. 1. The noise detection procedure for both positive and negative noise is as follows:

(i) We consider a neighborhood window W that is located at point x of the image f. Th e differences between the central pixel at point x and the pixel values of the n-1surr ounding points of the neighborhood (excluding thevalue of the central pixel) are co mputed.

(ii) The sum of the absolute values of these differences is computed, denoted as fabs(x ). This value provides ameasure of closeness between the central pixel and its su rrounding pixels.

(iii) The value fabs(x) is compared to fthreshold(x), which is anappropriately selected positive integer threshold value and can be modified. The central pixel is conside red to be noise when the value fabs(x) is greater than thethreshold value fthresho d(x).

(iv) When the central pixel is considered to be noise it is substituted by the median val ue of the image neighborhood,denoted as fk+1, which is the normal operationof the median filter. In the opposite case, the value of the central pixel is not altered and the procedure is repeated for the next neighborhood window.From the noised etection scheme described, it should be mentioned that the noise detection level procedure can be controlled and a range of pixel values (and not only the fixed

values of 0 and 255, salt and pepper noise) is considered asimpulse noise.In Fig. 2 the results of the application of the median filter and the CBMF in the gray-sca le image “Peppers” are depicted.More specifically, in Fig. 2(a) the original,uncor rupted image“Peppers” is depicted. In Fig. 2(b) the original imagedegraded by 5% both positive and negative impulse noise isillustrated. In Figs 2(c) and 2(d) the re

sultant images of the application of median filter and CBMF for a 3×3-pixel win dow are shown, respectively. Finally, the resultant images of the application of m edian filter and CBMF for a 5×5-pixelwindow are presented in Figs 2(e) and 2(f). It can be noticed that the application of the CBMF preserves much better edges a nddetails of the images, in comparison to the median filter.A number of different objective measures can be utilized forthe evaluation of these results. The most wi dely used measures are the Mean Square Error (MSE) and the Normalized Mean Square Error (NMSE) [1]. The results of the estimation of these measures for the two filters are depicted in Table I.For the estimation of these measures, the result ant images of the filters are compared to the original, uncorrupted image.From T able I it can be noticed that the MSE and NMSE estimatedfor the application of t he CBMF are considerably smaller than those estimated for the median filter, in all the cases.

Table I. Similarity measures.

3. HARDWARE ARCHITECTURE

The structure of the adaptive filter comprises four basic functional units, the mo ving window unit , the median computation unit , the arithmetic operations unit , and th e output selection unit . The input data of the system are the gray-scale values of the pi xels of the image neighborhood and the noise threshold value. For the computation of the filter output a3×3 or 5×5-pixel image neighborhood can be selected. Image input d ata is serially imported into the first stage. In this way,the total number of the inputpin s are 24 (21 inputs for the input data and 3 inputs for the clock and the control signalsr equired). The output data of the system are the resultant gray-scale values computed f or the operation selected (8pins).

The moving window unit is the internal memory of the system,used for storing th e input values of the pixels and for realizing the moving window operation. The pixel values of the input image, denoted as “IMAGE_INPUT[7..0]”, areimported into this u nit in serial. For the representation of thethreshold value used for the detection of a no Filter Impulse noise 5% mse Nmse(×10-2) 3×3 5×5 3×3 5×5

Median CBMF 57.554 35.287 130.496 84.788 0.317 0.194 0.718 0.467

ise pixel 13 bits are required. For the moving window operation a 3×3 (5×5)-pixel sep entine type memory is used, consisting of 9 (25)registers. In this way,

when the windo

P1 P2 P3

w is moved into the next image neighborhood only 3 or 5 pixel values stored in the m

emory are altered. The “en5×5” control signal is used for the selection of the size of th e image window, when“en5×5” is equal to “0” (“1”) a 3×3 (5×5)-pixel neighborhood is selected. It should be mentioned that the modules of the circuit used for the 3×3-pix el window are utilized for the 5×5-pixel window as well. For these modules, 2-to-1mu ltiplexers are utilized to select the appropriate pixel values,where necessary. The mod ules that are utilized only in the case of the 5×5-pixel neighborhood are enabled by th e“en5×5” control signal. The outputs of this unit are rows ofpixel values (3 or 5, respe ctively), which are the inputs to the median computation unit.The task of the median c omputation unit is to compute themedian value of the image neighborhood in order to substitutethe central pixel value, if necessary. For this purpose a25-input sorter is utili zeed. The structure of the sorter has been proposed by Batcher and is based on the use of CS blocks. ACS block is a max/min module; its first output is the maximumof the i nputs and its second output the minimum. The implementation of a CS block includes a comparator and two 2-to-1 multiplexers. The outputs values of the sorter, denoted a s “OUT_0[7..0]”…. “OUT_24[7..0]”, produce a “sorted list” of the 25 initial pixel val ues. A 2-to-1 multiplexer isused for the selection of the median value for a 3×3 or 5×5-pixel neighborhood.

The function of the arithmetic operations unit is to computethe value fabs(x), whi ch is compared to the noise threshold value in the final stage of the adaptive filter.The in puts of this unit are the surrounding pixel values and the central pixelof the neighb orhood. For the implementation of the mathematical expression of fabs(x), the circuit of this unit contains a number of adder modules. Note that registers have been used to achieve a pipelined operation. An additional 2-to-1 multiplexer is utilized for the selec tion of the appropriate output value, depending on the “en5×5” control signal. From th e implementation point of view, the use of arithmetic blocks makes this stage hardwar e demanding.

The output selection unit is used for the selection of the appropriateoutput value of the performed noise suppression operation. For this selection, the corresponding no ise threshold value calculated for the image neighborhood,“NOISE_THRES HOLD[1 2..0]”,is employed. This value is compared to fabs(x) and the result of the comparison Classifies the central pixel either as impulse noise or not. If thevalue fabs(x) is greater than the threshold value fthreshold(x) the central pixel is positive or negative impulse noise and has to be eliminated. For this reason, the output of the comparison is used as the selection signal of a 2-to-1 multiplexer whose inputs are the central pixel and the c orresponding median value for the image neighborhood. The output of the multiplexer is the output of this stage and the final output of the circuit of the adaptive filter.The st ructure of the CBMF, the computation procedure and the design of the four aforeme n tioned units are illustrated in Fig. 3.

Image

windoe

Figure 1: Block diagram of the filtering method

Figure 2: Results of the application of the CBMF: (a) Original image, (b) noise corrupted image (c) Restored image by a 3x3 MF, (d) Restored image by a 3x3 CBMF, (e) Restored image by a 5x5 M

F and (f) Restored image by a 5x5 CBMF.

4. IMPLEMENTATION ISSUES

The proposed structure was implemented in FPGA,which offer an attractive com bination of low cost, high performance and apparent flexibility, using the software pa ckage+PLUS II of Altera Corporation. The FPGA used is the EPF10K200SFC484-1 d evice of the FLEX10KE device family,a device family suitable for designs that requir e high densities and high I/O count. The 99% of the logic cells(9965/9984 logic cells) of the device was utilized to implement the circuit . The typical operating clock frequ ency of the system is 55 MHz. As a comparison, the time required to perform filtering of a gray-scale image of 260×244 pixelsusing Matlab? software on a Pentium 4/2.4 G Hz computer system is approximately 7.2 sec, whereas the corresponding time using h ardware is approximately 10.6 msec.The modification of the system to accommodate windows oflarger sizes can be done in a straightforward way, requiring onlya small nu mber of changes. More specifically, in the first unit the size of the serpentine memory P4P5P6

P7P8P9

Subtractor

arry

Median

filter

adder compara

tor

muitiplex

er

f abc(x)value

and the corresponding number of multiplexers increase following a square law. In the second unit, the sorter module should be modified,and in the third unit the number of the adder devicesincreases following a square law. In the last unit no changes are requ ired.

5. CONCLUSIONS

This paper presents a new hardware structure of a content based median filter, ca pable of performing adaptive impulse noise removal for gray-scale images. The noise detection procedure takes into account the differences between the central pixel and th e surrounding pixels of a neighborhood.The proposed digital circuit is capable ofproce ssing grayscale images of 8-bit resolution, with 3×3 or 5×5-pixel neighborhoods as op tions for the computation of the filter output. However, the design of the circuit is dire ctly expandableto accommodate larger size image windows. The adaptive filter was d eigned and implemented in FPGA. The typical clock frequency is 55 MHz and the sys tem is suitable forreal-time imaging applications.

REFERENCES

[1] W. K. Pratt, Digital Image Processing. New York: Wiley,1991.

[2] G. R. Arce, N. C. Gallagher and T. Nodes, “Median filters:Theory and applica

t ions,” in Advances in ComputerVision and Image Processing, Greenwich, CT: JAI, 1986.

[3] T. A. Nodes and N. C. Gallagher, Jr., “The output distributionof median type filt

e rs,” IEEE Transactions onCommunications, vol. COM-32, pp. 532-541, May

1984.

[4] T. Sun and Y. Neuvo, “Detail-preserving median basedfilters in image

processing,” Pattern Recognition Letters,vol. 15, pp. 341-347, Apr. 1994.

[5] E. Abreau, M. Lightstone, S. K. Mitra, and K. Arakawa,“A new efficient approach

for the removal of impulsenoise from highly corrupted images,” IEEE Transa ctionson Image Processing, vol. 5, pp. 1012-1025, June 1996.

[6] E. R. Dougherty and P. Laplante, Introduction to Real-Time Imaging, Bellingham:

SPIE/IEEE Press, 1995.

二、英文翻译

基于中值滤波的新的内容

摘要

在本设计中的提出了基于中值滤波的硬件实现用来抑制脉冲噪声的干扰。拟议的电路功能是自适应；只有在必要时它才检测到存在邻域图像中的脉冲噪声中并应用中值滤波运算符。用这种方式，可以避免使图像在处理过程中变模糊并且完整的保留了图像边缘和细节的重要信息。拟议的数字硬件结构是能够处理的8位灰度图像决议和完全流水线，而将用来并行处理计的算时间降至最低，FPGA 的现场可编程结构和其在图像处理领域的应用，在这些领域内快速的运算是极为重要的。典型的系统时钟频率为55Mhz.

1 介绍

在图像处理过程中有非常重要的应用程序分别是噪声过滤和图像增强。在图像处理过程中这些都是很必要的，不管最终图像是否用于视觉解释或自动分析。噪声滤波的目的是消除噪声及对原始图像影响，同尽可能少的时损坏原来的图像。因此非线性（例如中值滤波哦，一般情况下的顺序统计滤波器）与线性的滤波器先比，提供了更令人满意结果。脉冲噪声存在许多实际的应用程序中，并且来源广泛，包括一些人类的行为，例如作为保护的开关、工业机械、和汽车点火系统。由于嘈杂的传感器或通道传输错误的脉冲噪声早造成工图像的损坏。最常见用于灰度和彩色图像脉冲噪声抑制的方法是中值滤波。中值滤波的缺点是在应在模糊图像的滤波过程中。在一般情况下，筛选器是将应用于整个图像，修改的像素的均匀从而不受噪音污染。这种方式，有效的消除脉冲噪声往往是以整体图像的模糊或扭曲功能的退化为代价的。在本设计中提出的一种基于有效率才不脉冲噪声的中值滤波器上提出的。提出的电路电路具有只有当检测到当前图像中存在噪声时才启动相应的中值滤波程序。噪音检测过程基于图像的内容和计算中央像素和

周围的邻居的像素之间的差异。这种自适应的方法主要优势是避免图像的模糊和保留边缘详细信息的完整性。这种数字硬件结构是有能够进行8位分辨率的灰

度图象处理和执行这两个正面和负面的脉冲噪声去除。选择这种电路结构是基于四种基本的系列功能和在每一个阶段并行执行的流水线功能。但是，系统可以轻松地扩展以容纳更大的窗口大小。这个结构将使用可编程门阵列。数字电路的设计在由Altera 公司提供的的可编程逻辑发展系统MAX+plus ii下成功编译和仿真。本系统将使用EPF10K200SFC484-1 FPGAFLEX10KE系列的芯片。其时钟频率是55MHZ并且此系统能被用于实时成像应用程序，在此系统中快熟处理是很必要的。例如对一张像素为260 × 244灰度图像所需要的时间为10.6毫秒。

2.自适应的筛选过程

图像f点的中值输出取决于图像附近x点的值。这个小区由w的窗口，位

与n = 2 k + 1个。自适应内容基于中值滤波器器可用于点f附近有n 个x1，x

2

于脉冲抑制灰度图像的噪声。自适应滤波的块状图过程如图 1 所示。噪声检测过程正面和负面的噪音如下所示：

（一）我们认为附近窗口w位于图像f的x点处，同x点的差异并计算其附近的n-1个像素值，包括中间值。

(二) 这些差异的总和是绝对值的计算，用fab（x）表示。这个值提供了中间点

和周围点之间最密切的关系。

（三）将值fabs(x)与fthreshold(x)进行对比，看哪一个更适合作为正确的正整数阈值并可以进行修改。当fans（x）大于阈值值fthreshold(x)时中央像素就会被认为是噪声。

（四) 中央像素被认为是噪音时，将被附近的中值所取代，及fk+1是中值滤波器的正常输出值。在相反的情况下，中间像素值不会改变，并重复该过程下一步的邻域窗口。从所述的噪声检测方案中，应该提到噪声检测水平过程是可以控制，并且像素值的范围（和不只固定值0和255，盐和胡椒粉噪音）被认为是脉冲噪声。在图 2 中位数的应用程序的结果进行筛选和描述"椒盐"的灰度图像中的 CBMF。更具体地说，在图 2 (a)的原始的、未损坏的图像描述"椒盐"。在图2 (b) 原始图像5%，这两个正面和负面的脉冲噪声被降级说明了。无花果2(c) 和2(d)的结果图像3 × 3 像素窗口中值滤波和CBMF中的应用显示，分别。最后，结果的图像5 ×5 像素的中值滤波和CBMF的应用窗口载列于无花果2(e)和 2 (f)。相比中

值滤波器可以看到CBMF的应用可以保留好得多边缘和细节的图像。可以利用一些不同的客观措施来评价这些结果。使用最广泛的方法是均方错误(MSE)和规范化均方误差（NMSE）。对这些措施的结果的评估对比如表一。对于这些措施的评估输出的图像将与原始的、为受损的图像进行比较。从表可以看出对于CBMF的MSE和NMSE评估在任何情况下比所有的中值滤波器都要小。

3硬件结构体系

自适应滤波器的结构包括四个基本功能单位、移动窗口单位、中位数的计算单位、算术运算的单位与输出选择单位。输入的系统的数据是图像邻域灰度值像素和噪声的阈值。滤波器的输出可以选择 3 × 3或 5 × 5图像附近的像素点。第一阶段是输入的数据以串行方式导入。这种方式，输入引脚的总数是24 （21投入输入的数据和时钟和控制信号3投入必需）。

输出数据是系统的合力灰度值计算的所选操作（8针脚）。用于存储输入的像素值和实现移动窗口操作的是移动窗口模块内部的内存系统。像素值输入的图像IMAGE_INPUT[7..0]，简称能量吸收率是导入到此序列中的单元，表示用来检测噪声像素的13位的阈值是必要的。对于一个3 × 3 (5 × 5）的移动窗口操作使用像素蛇纹石类型的内存，组成的9 (25)注册。使用这种方式当窗口一如的图像领域仅有3 或5像素值存储是内存就会发生改变。使用"en5 ×5"控制信号选择图像窗口的大小的时en5 × 5等于0(1)3 × 3 (5 × 5）像素邻域处于选中状态。对于这些模块，当有需要时2-1多路复用器要选择适当的像素值。只有在使用5 × 5像素领域模块时，使能信号en5 × 5才为1。此单元的输出是行的像素值（3 或5，分别），即那些计算单元的输入值。计算单元的任务就是，当如有必要时计算图像领域的中值一边代替中心点的像素值。为了达到此效果我们需要利用25输入排序器。直到Batcher提出排序器结构，且其基于CS块的使用。一个CS 模块是一个最大/最小模块；它的第一个输出是输入的最大值，第二个输出是是最小值。CS模块实施包括一个比较器和一个2选1多路复用器。排序器的输出用OUT_0[7..0]、OUT_24[7..0]，并作出25 初始像素值列表。2选1多路复用器用于 3 × 3 或5 × 5领域像素中值的输出。

算术运算单元的功能是计算值fabs(x)，即通过比较作为自适应滤波器噪声阀值输出最后阶段的中值。输入单元的值是中央相熟周围领域的值。对于fab（x）数学表达式执行，此电路的数学表达式包括很多加法器模块。需要注意的是需要用到寄存器用来实现流水线的操作。另外2选1多路复用器被利用于相应的选择输出值，这就要取决于en5 × 5控制信号。从实施点方向看，算数快的使用适当的这一部分的电路满足要求。对于此选择，相应的噪声阀值是为相应的噪声阀值所计算，采用NOISE_THRESHOLD[12..0]表示。此值是同fabs(x)和比较的结果进行分类不论是作为脉冲噪声或不的中央像素相比较的。如果fabs(x)值大于阈值fthreshold(x)则中央的像素是积极的还是消极的脉冲噪声并且需要要被淘汰。为此，比较的输出将被用作2选1多路复用器的信号，即输入信号是中央中央像素和是图像领域相关的中值。多路复用器的输出是输出阶段和适应滤波器电路的最终输出。CBMF的结构、计算过程的结构和上述所说的四个单元的设计如图3所示。

4.执行问题

目标结构将在FPGA上实现，FPGA结构具有低成本、高性能和相对灵活性的特点，并有使用Altera 公司的Max+plus ii软件包的功能。使用FPGA的EPF10K200SFC484-1装置的FLEX10KE 设备系列适用于要求高的密度的设计的设备系列和高I/O 计数。设备99%的逻辑单元格（9965/9984逻辑单元格）被用作电路的实施。系统的时钟频率为55MHZ。作为比较，执行的260 × 244像素的灰度图像的筛选奔腾4/2.4GHZ 的计算机上使用Matlab ?软件需要的时间的系统时间是约为7.2 秒，而相应的使用硬件的时间是约10.6 毫秒。系统的改造是为了以容纳windows系统的改造，要求只要少量的更改，就可以简单的方式做较大的改变。更具体地说，在第一单元的蛇纹石的内存和相应的大小后一个正方形的多路复用器增加的数目的定律。在第二个单位中，应修改排序器模块，并且在第三单元加法器增加的增加以下平方的规则。在最后一个单元中是没必要没有变化的。

5.结论

本文介绍的内容是基于中值滤波的新硬件的结构，是一种能够执行自适应的