中值滤波器脉冲噪声毕业论文中英文资料外文翻译文献
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 FILTERABSTRACTIn 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. INTRODUCTIONTwo 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 MFonly 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 PROCEDUREThe 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 fixedvalues 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 resultant 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 ARCHITECTUREThe 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 5×5Median CBMF 57.554 35.287 130.496 84.788 0.317 0.194 0.718 0.467ise 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 windoP1 P2 P3w is moved into the next image neighborhood only 3 or 5 pixel values stored in the memory 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.ImagewindoeFigure 1: Block diagram of the filtering methodFigure 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 MF and (f) Restored image by a 5x5 CBMF.4. IMPLEMENTATION ISSUESThe 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 P4P5P6P7P8P9SubtractorarryMedianfilteradder comparatormuitiplexerf abc(x)valueand 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. CONCLUSIONSThis 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 applicat 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 filte rs,” IEEE Transactions onCommunications, vol. COM-32, pp. 532-541, May1984.[4] T. Sun and Y. Neuvo, “Detail-preserving median basedfilters in imageprocessing,” Pattern Recognition Letters,vol. 15, pp. 341-347, Apr. 1994.[5] E. Abreau, M. Lightstone, S. K. Mitra, and K. Arakawa,“A new efficient approachfor 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。
去除红外图像中脉冲噪声的双边加权中值滤波
去除红外图像中脉冲噪声的双边加权中值滤波
顾冬娟
【期刊名称】《传感技术学报》
【年(卷),期】2024(37)3
【摘要】针对现有方法在去除红外图像的脉冲噪声时,未能有效保持图像的边缘细节和纹理结构,提出了基于统计检测的双边加权中值滤波算法。
算法根据脉冲噪声的取值和分布特征,用最小和最大像素值以及统计规律进行噪声检测;对检测出来的噪声像素,以多尺度的方式、自适应地用双边加权系数对邻域中的无噪像素和已经去噪处理的像素进行频次加权,然后取它们的中值作为当前噪声像素的估计值。
其中双边加权系数自适应于距离邻近度与灰度相似度。
实验结果表明,相对于部分现有方法,所提方法去噪所得的EPI和SSIM值更高,去噪图像的视觉效果更佳。
【总页数】7页(P492-498)
【作者】顾冬娟
【作者单位】江西机电职业技术学院教务处
【正文语种】中文
【中图分类】TP391
【相关文献】
1.一种有效去除图像中脉冲噪声的滤波算法
2.一种去除图像中脉冲噪声的滤波算法
3.利用几何结构检测去除图像中的随机值脉冲噪声
4.脉冲噪声污染图像中的自适应中值滤波器
5.一种新的去除红外图像疵点的加权平均算法
因版权原因，仅展示原文概要，查看原文内容请购买。
文献翻译(中文)
对多脉冲噪声的自适应阈值中值滤波通信与信息工程学院，电子科技大学成都中国中国61005与技术学院抽象衰减噪声在图像处理中起重要作用。
几乎所有的传统中值滤波器涉及去除具有单个层，其噪声灰度值是恒定的脉冲噪音。
在本文中，一种新的自适应中值滤波，提出了处理这些不仅是单层噪声的图像。
自适应阈值滤波器（ATMF）已开发通过组合自适应中值过滤器（AMF）和两个动态阈值。
动态门限的，因为正在使用，ATMF是能够平衡在除去多脉冲噪声和图像的质量。
提供该方法与传统的中值滤波的比较。
一些视觉实施例用来表明所提出的滤波器的性能。
关键词：中值滤波;自适应中值过滤器（AMF）;自适应阈值中值滤波器（ATMF）;多脉冲噪声;影像处理图像往往是由脉冲噪声是由于来自传感器或交际渠道产生的错误损坏。
它的边缘检测，图像分割和目标识别过程之前，以消除图像中的噪点是非常重要的。
众所周知的中值滤波器（MF）和它的衍生物已被确认为去除脉冲噪声的有效手段。
中值滤波器的成功是基于两个主要性能：边缘保持高效的噪声衰减，随着对冲动型噪声的鲁棒性。
边缘保持在图像处理必不可少由于视觉感知[7]的性质。
尽管它在平滑噪声效能，MF倾向于当应用于图像均匀地除去细的细节。
为了消除这个缺点，一个著名的改性的中值滤波，自适应中值过滤器（AMF），已经提出了。
它具有可变的窗口大小去除脉冲同时保留锐度同时。
以这种方式，边缘信息和详细信息的完整性变得更好。
上面提到的过滤器不善于去除多脉冲噪声。
然而，实际情况是，图像是由多脉冲噪声，包括单层噪声经常被破坏。
在本文中，一个基于决策的和信号自适应中值滤波算法。
它不仅实现脉冲噪声均强检测和视觉质量恢复的结果，但也确实很好地抗多的噪音。
对于噪声的识别，新的标准已在AMF加入，以使效果处理多个噪声。
此后，新的过滤器，命名为自适应阈值中值滤波器（ATMF），增加了当地的内核区域的两个动态阈值来帮助检测噪音。
仿真结果表明，该过滤器是一样好AMF的一层脉冲噪声，但比其他许多中值滤波器更好的为多脉冲噪声。
音频信号处理博士论文中英文资料外文翻译文献
音频信号处理博士论文中英文资料外文翻
译文献
音频信号处理是一个广泛研究的领域，涉及到音频信号的获取、分析、传输和处理等方面。
本文翻译了以下两篇外文文献，为音频
信号处理博士论文的写作提供参考。
文献一：Title of Paper One
作者：
摘要：
该篇文献提出了一种新的音频信号处理算法，旨在改善音频信
号的质量和增强用户对音乐的感受。
通过对音频信号进行特征提取
和分析，该算法能够有效地消除噪音和失真，并提供更清晰、更丰
富的音频体验。
文献介绍了算法的原理和实现方式，并通过实验验
证了其在不同音频数据集上的有效性。
文献二：Title of Paper Two
作者：
摘要：
该篇文献探讨了音频信号处理领域的一个重要问题，即语音识
别的准确性和鲁棒性。
通过分析现有的语音识别算法，文献指出了
当前算法存在的一些问题，并提出了一种改进的方法。
该方法基于
深度研究和卷积神经网络，并通过对音频信号进行多层次的特征研
究和表示研究，提高了语音识别的准确性和鲁棒性。
文献还介绍了
该方法的实验结果，并与其他算法进行了比较。
总结
这两篇外文文献介绍了音频信号处理领域的一些重要研究进展
和算法。
它们提供了宝贵的参考和借鉴，可以在音频信号处理博士
论文的写作中起到指导作用。
通过综合运用这些研究成果，我们可
以进一步改进音频信号处理算法，提高音频信号的质量和用户体验。
(完整版)基于中值滤波和小波包变换的低剂量CT图像的去噪毕业设计
密级：NANCHANG UNIVERSITY学士学位论文THESIS OF BACHELOR（2010—2014年）题目基于中值滤波和小波包变换的低剂量CT图像的去噪学院：信息工程学院系电子信息工程专业班级：生物医学工程101班指导教师：朱莉职称：讲师起讫日期：2014年3月10日—2014年5月30日南昌大学学士学位论文原创性申明本人郑重申明：所呈交的论文是本人在导师的指导下独立进行研究所取得的研究成果。
除了文中特别加以标注引用的内容外，本论文不包含任何其他个人或集体已经发表或撰写的成果作品。
对本文的研究作出重要贡献的个人和集体，均已在文中以明确方式表明。
本人完全意识到本申明的法律后果由本人承担。
作者签名：日期：学位论文版权使用授权书本学位论文作者完全了解学校有关保留、使用学位论文的规定，同意学校保留并向国家有关部门或机构送交论文的复印件和电子版，允许论文被查阅和借阅。
本人授权南昌大学可以将本论文的全部或部分内容编入有关数据库进行检索，可以采用影印、缩印或扫描等复制手段保存和汇编本学位论文。
保密□，在年解密后适用本授权书。
本学位论文属于不保密□。
（请在以上相应方框内打―√‖）作者签名：日期：导师签名：日期：基于中值滤波和小波包变换的低剂量CT图像的去噪学生姓名：叶红指导教师：朱莉摘要随着科技的进步，现代医学影像学事业突飞猛进，计算机断层扫描(Computed Tomography，CT)技术已成为医学影像学的核心技术之一。
尽管CT图像的不少技术指标还赶不上传统X射线屏-片图像，但CT成像方法克服了传统屏-片成像射线方向信息重叠的局限，加之CT图像足够的清晰度和准确性使得CT成为临床诊断成像中应用最广泛的方法之一。
为了得到高质量的医学图像，很多CT设备加大X线剂量，但照射剂量偏高，不仅会提高CT设备的运行成本，还会对患者的身体造成伤害。
因此，临床多采用降低剂量的扫描方案，但低剂量CT扫描时受到量子噪声的影响，使得图像质量下降，影响诊断的准确性。
毕业论文-基于中值滤波的图像椒盐噪声滤除算法的研究与应用
本科生毕业论文（设计）题目：基于中值滤波的图像椒盐噪声滤除算法的研究与应用院系：信息科学与技术学院专业：计算机科学与技术学生姓名：**学号：********指导教师：罗笑南(教授)（职称）二〇年月基于中值滤波的图像椒盐噪声滤除算法的研究与应用摘要摘要在现实生活中，将实际获得的图像进行数字化，并在其传输和处理过程中，不可避免的存在着一定的外部干扰和内部干扰，使用户所获得的数字图像被噪声污染，而这些噪声中椒盐噪声的表现更为明显。
为此有大量学者针对椒盐噪声进行研究分析。
中值滤波算法以其非线性的特点，在处理椒盐噪声上有显著的效果，因此许多学者针对中值滤波算法进行改进。
典型的如加权中值滤波器、开关中值滤波器、自适应中值滤波器等。
然而这些滤波器或在保护图像细节上能力不足，或去除噪声效果不佳，或对于高密度噪声无法处理，或过于复杂不便于硬件实现，存在着一定的缺陷。
本文研究了几种典型的改进后的中值滤波算法，通过理论分析与实验仿真，比较其在去噪及保护图像细节各方面的优缺点。
在此基础上，本文给出了一种基于噪声检测的模糊自适应中值滤波算法。
在该算法中，通过设计窗口自适应的噪声检测机制，有效的区分了噪声与非噪声像素点，从而高效的保护了图像细节信息，并大量减少需要处理的像素点，降低算法运行时间。
同时对于检测出的噪声点，则采用改进了的自适性中值滤除算法进行噪声滤除，在该滤波算法中加入了窗口自适应控制，对于高密度噪声也能有效的进行去噪处理。
通过详细的理论分析后，本文基于matlab进行大量仿真实验，验证了这些新方法在噪声去除与细节保留上的有效性。
关键词：图像去噪、椒盐噪声、噪声检测、自适应中值滤波、开关阈值滤波AbstractThe acquisition, recording and transmission of digital images through sensors or communication channels are often interfered by different types of noises, which may change the image. Impulse noise is one most common and important kind of noise. Impulse noise removal in image processing is an important pre-processing so that many researchers work on the restoration of images corrupted by impulse noise. Being the most popular nonlinear filter, the median filter is often used to remove impulse noise because of its good denoising power. In that case, many image-denoising filters are proposed based on the median filter, such as the weighted median filter, soft switching filter, adaptive median filter, etc.In this paper, a new image-denoising filter that is based on several advanced median filter is proposed. There is a two-phase scheme in this new algorithm. In the first phase, an impulse noise detection is used to identify pixels which are likely to be noise candidates. The noise detection has variable window size for removal of impulses, so that we can keep most of the signal content of the uncorrupted pixels, and time used for filtering can be reduced by a wide margin. In the second phase, the noise candidates will be filtered by the new filter. Based on the adaptive median filter, the proposed filter is superior to some other filters mentioned in this paper not only for smooth pictures but also images that are complicated and have many sharp edges. Being incorporated with variable windows size, our method is also very useful for images with high noise level.Key Words:image denoising, salt-and-pepper noise, adaptive median filter,impulse noise detector, switching-based median filter目录第一章引言 (1)1.1课题背景及其意义 (1)图像滤波技术概述 (1)中值滤波研究现状 (2)本论文的主要工作 (3)1.3论文章节安排 (4)第二章图像去噪算法综述 (5)2.1图像去噪方法概述 (5)2.2图像噪声模型 (6)2.3图像去噪质量的评估方法 (7)2.4中值滤波 (8)2.5维纳滤波 (9)2.6均值滤波 (11)2.7其他滤波技术 (12)2.8小结 (13)第三章几种中值滤波去噪方法分析 (14)3.1标准中值滤波方法(STANDARD MEDIAN FILTER,SM) (14)3.2带权值的中值滤波方法(WEIGHTED MEDIAN FILTER) (15)3.3三态中值滤波方法(TRI-STATE MEDIAN FILTER) (17)3.4自适应软开关滤波方法 (18)3.5自适应中值滤波方法 (20)3.6实验结果分析 (21)3.7小结 (26)第四章基于噪声检测的自适应中值滤波 (27)4.1噪声检测机制 (27)4.2椒盐噪声滤除方法 (33)4.2.1 噪声滤除策略 (34)4.2.2 动态窗口策略 (35)4.2.3 VAM滤波方法 (37)4.3小结 (37)第五章仿真结果分析比较 (39)5.1噪声检测机制性能分析 (39)5.2V AM滤波器去噪效果分析 (41)5.3小结 (45)第六章结语 (46)6.1论文主要工作总结 (46)6.2展望 (46)参考文献 (48)致谢 (50)第一章引言1.1 课题背景及其意义冈萨雷斯曾在其著作中提到，视觉是人类感觉中最高级的，而图像又在人类的感知中起着重要的作用[1]。
一种有效去除图像中脉冲噪声的滤波算法
一种有效去除图像中脉冲噪声的滤波算法谭筠梅;王履程;鲁怀伟【摘要】为了抑制脉冲噪声,根据脉冲噪声点和边缘像素点的特征,提出了一种新的基于脉冲噪声点检测的滤波算法.实验结果表明,与传统中值滤波相比,这种新算法很好地保留了图像的细节,尤其在噪声密度较低时,滤波性能更加优越.%In order to suppress the impulse noise,a new filter based on the impulse noise point detection is presented according to the characteristics of the impulse noise points and the edge pixels. Experimental results show that this new algorithm preserves good image detail compared with the traditional median filter,especially superior filtering performance when the noise density is low.【期刊名称】《兰州交通大学学报》【年(卷),期】2011(030)001【总页数】4页(P18-21)【关键词】脉冲噪声;中值滤波;噪声点检测【作者】谭筠梅;王履程;鲁怀伟【作者单位】兰州交通大学,电子与信息工程学院,甘肃,兰州,730070;兰州交通大学,电子与信息工程学院,甘肃,兰州,730070;兰州交通大学数理与软件工程学院,甘肃,兰州,730070【正文语种】中文【中图分类】TP751.10 引言在实际应用中,图像不可避免地被脉冲噪声污染,脉冲噪声是由图像传感器、传输信道、解码处理等产生的黑白相间的亮暗点噪声,会严重影响图像的质量.用于工程方面的图像往往对质量要求非常高,图像的细节应尽可能的完整清晰,以便进一步对图像进行分割、特征提取等操作,因此抑制脉冲噪声、提高信噪比,保留原图像的完整性和边缘信息,一直是图像预处理的重要环节.去除脉冲噪声最常用的是传统中值滤波算法,由于简单易实现使得它在图像处理中得到了广泛的应用.但是,传统的中值滤波在滤除噪声的同时损失了图像的细节,特别当滤波窗口选得比较大时更会如此.这主要是由于中值滤波对图像中的所有像素点进行了统一处理,这样,一方面容易把图像中的边缘细节点当成了噪声点,进行了滤波处理;另一方面造成了噪声在邻域的传播,削弱了算法保留原图像细节的能力[1].为了克服中值滤波的这个缺点,人们提出了多种改进算法,如双态中值滤波(TSM)算法[2]、M in-max滤波算法[3]、模糊脉冲噪声检测及去噪方法(FIDRM)[4]、方向加权中值滤波(DWM)[5]等.这些算法相对于传统中值滤波去噪性能得到很大提高,但是并没有很好的解决图像细节模糊的问题.本文提出一种新的方法来判别像素点是噪声点还是边缘像素点,然后再进行滤波,从而保留原图像的完整性和边缘信息.1 算法实现与图像信号的强度相比,脉冲干扰通常幅值较大,因此在一幅图像中,脉冲噪声可以数字化为图像灰度值的最大最小值,负脉冲噪声以黑点,正脉冲噪声以白点出现在图像中[6].因此本文算法首先筛选出滤波窗口内像素值为极值点的像素点作为备选点.图像的边缘是指图像局部亮度变化最显著的部分,即在灰度级上发生急剧变化的区域.从空域角度看,二维图像上的边缘相邻像素灰度从某一个值跳变到另一个差异较大的值.因此脉冲噪声点和边缘像素点都可能是备选点,但是由于边缘是一组相连的像素集合,这些像素位于两个不同的平滑图像区域之间,而脉冲噪声点与邻域像素点相比一般是具有非常大或非常小的灰度值的孤立点,因此可以进一步区分备选点是脉冲噪声点还是边缘像素点.本算法分两步实现,第一步采用两种方法检测出噪声点;第二步对噪声点进行滤波.1.1 噪声检测1.1.1 噪声点筛选脉冲噪声的概率密度函数可由公式(1)描述式中:f(x,y)为原始图像在点(x,y)处的灰度值;[]为原始图像的像素点的动态范围;fi(x,y)为噪声污染图像在点(x,y)处的灰度值;P为脉冲噪声的密度.根据脉冲噪声的极值特性,首先利用初步筛选脉冲噪声点,基本思路如下[7]:设Wxy是以点(x,y)为中心的滤波窗口,为中的灰度最小值,fimax为Wxy中的灰度最大值.首先找出每一个的最大值和最小值,如果 fi(x,y)介于最小值和最大值之间,那么该点是一个非脉冲点,否则该点为脉冲噪声点,记为1.1.2 噪声点确定对于上述方法筛选出的噪声点,本文提出以下算法利用两个规则进一步区分它是脉冲噪声点还是边缘点.使用(2K+1)×(2K+1)(取K=1)的矩形窗,对于中心像素 fi(x,y)来说其具有八个不同方向的相邻像素[8],这八个方向是Direction{NW,N,NE,ES,E,S,SW,W},如图1所示.fi(x+k,y+ l)是对应于fi(x,y)的不同方向的相邻像素,其中: k,l∈{-K,…,+K},如fi(x-1,y+1)是fi(x, y)在NE方向的相邻像素,不同方向的k与l的取值见表1.计算出fi(x,y)与这八个相邻像素的梯度值[6],记为图1 像素的八个相邻方向Fig.1 Thepixel'seightneighbordirections表1 计算各方向的梯度所用的像素Tab.1 Pixelsusedtocalculatethegradientofthe differentdirections方向D (k,l)的取值两个相邻像素NW (-1,-1)fi(x-1,y+1),fi(x+1,y-1) W (0,-1) fi(x-1,y),fi(x+1,y) SW (1,-1) fi(x+1,y-1),fi(x+1,y+1) S (1,0) fi(x,y-1),fi(x,y+1) SE (1,1) fi(x-1,y+1),fi(x+1,y-1) E (0,1) fi(x-1,y),fi(x+1,y) NE (-1,1) fi(x-1,y-1),fi(x+1,y+1) N (-1,0) fi(x,y-1),fi(x,y+1)这八个梯度值称为每个方向的基本梯度值,除此以外还要使用与中心像素 fi(x,y)成直线并垂直于该方向的另外两个相邻像素的梯度值,这两个相邻像素的选择见表1.例如NE方向,还应计算像素fi(x-1,y-1)和fi(x+1,y+1)在NE方向的梯度值,如图2所示.把基本梯度值和另外两个相关的梯度值的绝对值分别定义为▽Dfi(x,y),fi(x, y)和fi(x,y),其中:D∈Direc tion{NW,N, NE,ES,E,S,SW,W}.图2 计算NE方向的梯度值的像素Fig.2 Pixelsusedtocalculatethegradientofthe NEdirection下面利用以下两个规则,通过判断3个梯度值的大小来确定像素在对应方向是否被噪声污染.规则1 可如下定义中心像素fi(x,y)在某一方向被噪声污染如果((▽Dfi(x,y)＜T)并且(fi(x,y)＞T)并且(fi(x,y)＞T))或者((▽Dfi(x,y)＞T)并且(fi(x,y)＜T)并且(fi(x,y)＜T))那么在方向D上fi(x,y)是脉冲噪声,记为规则2 可如下定义中心像素fi(x,y)在某一方向未被噪声污染如果((▽Dfi(x,y)＞T)并且(fi(x,y)＞T)并且(fi(x,y)＞T))或者((▽Dfi(x,y)＜T)并且(fi(x,y)＜T)并且(i(x,y)＜T))那么在方向D上fi(x,y)未被脉冲噪声污染,记为这里T为阈值.像素(x,y)为(2K+1)×(2K+ 1)矩形窗的中心像素,则它与相邻像素的平均差值为T可以定义为(2K+1)×(2K+1)矩形窗内坐标(x,y)与中心像素点的上的h(x,y)差值的平均值:对于第一种方法检测出的噪声点,通过以上计算,如果则断定该点是脉冲噪声污染的像素点,否则为边缘点,记为1.2 噪声滤波噪声滤波时,如果滤波窗口的中心点已判断是脉冲噪声污染的像素点,则在该窗口内去除所有被噪声污染的点,中心点的灰度值等于剩余的未被噪声污染的点的中值.具体步骤如下:1)设W是以点(x,y)为中心的(2K+1)× (2K+1)滤波窗口,如果flag2(x,y)=0,则不进行任何处理,令fo(x,y)=fi(x,y),否则进行下一步;2)令S(i)为W窗口内所有未被噪声污染的点的集合,则fo(x,y)=Med(S(i)).3)最后令fo(x,y)为滤波输出的结果.2 实验与结果分析本文以512×512×8的Barbara图像和512× 512×8的mandrill图像(代表纹理细节丰富的图像)为例,将本文算法与传统中值算法进行比较.采用图像峰值信噪比(Peak Signal to Noise Ratio,PSNR)作为客观评价尺度[9],PSNR定义如下:本文算法与传统中值算法(3×3和5×5窗口)的PSNR值比较如表2,可见本文算法优于传统中值算法.为了更直观地进行滤波效果的比较,图3给出了噪声强度为0.1时三种算法对Barbara图像的输出图像以及图像中椅背的局部效果图,显然本文算法较传统算法在滤除噪声的同时更好的保留了图像细节.表2 不同噪声密度下降噪性能(PSNR)的比较Tab.2 Comparison of the denoising results for different noise intensities噪声密度(%) 传统中值(3×3)传统中值(5×5) 本文算法5 Barbara 25.073 8 23.163 1 31.790 7 Mandrill 28.174 4 24.632 8 33.392 8 10 Barbara 24.749 9 23.087 7 29.791 9 Mandrill 27.980 2 24.553 3 31.716 5 15 Barbara 24.769 4 23.082 3 28.958 2 Mandrill 26.562 8 24.951 6 30.002 1 20 Barbara 23.595 5 22.810 4 26.025 6 Mandrill 24.4637 23.489 4 25.148 9 30 Barbara 22.051 3 21.181 4 22.056 5 Mandrill 22.4368 21.562 3 22.684 1图3 各种滤波算法对Barbara的输出图像Fig.3 Output images by using various algorithms3 结论本文算法首先初步筛选出脉冲噪声备选点,在此基础上利用方向梯度值进一步区分出这些像素点是噪声点还是边缘点,然后只对噪声点进行滤波处理,从而减少了滤波后图像的模糊并较好的保留了图像的纹理细节.经仿真实验比较,该算法滤波效果都明显优于传统中值滤波,尤其是在噪声密度较低时滤波性能更加优越,当脉冲噪声密度很大时与传统滤波器相比滤波性能相当,但细节保留能力更强.参考文献:【相关文献】[1] 赵甘露,李小民,江涛,等.一种新型噪声检测中值滤波算法[J].计算机工程与科学,2006,11(28):30-32.[2] Chen T,Ma K K,Chen L H.Tri-statemedian filter for image denoising[J].IEEE T rans on Image Processing, 1999,8(12):1834-1838.[3] Wang Junghua,Lin Lianda.Imp roved median filter using m inmax algorithm for image processing[J].Electronics Letters,1997,33(16):1362-1363.[4] Schulte S,Nachtegael M.A fuzzy im pu lse noise detection and reductionmethod[J].IEEE Transac tions on image p rocessing,2006,15(5):1153-1162.[5] Dong Yiqiu,Xu Shufang.A new directional weighted median Filter for removal of random-valued im pu lse noise[J].IEEE Signal Processing Letters,2007,14 (3):193-196.[6] 冈萨雷斯.数字图像处理[M].2版.北京:电子工业出版社,2008.[7] 邢藏菊,王守觉,邓浩江,等.一种基于极值中值的新型滤波算法[J].中国图像图形学报,2001(6):533-536.[8] 张斌,宋旸,贺安之,等.基于任意方向图像导数算法的边缘检测技术[J].光电工程,2009(10):124-128.[9] 王履程,王静,谭筠梅,等.基于噪声强度估计的中值滤波[J].兰州交通大学学报,2008,6(6):112-114.。
信号处理中英文对照外文翻译文献
信号处理中英文对照外文翻译文献(文档含英文原文和中文翻译)译文：一小波研究的意义与背景在实际应用中，针对不同性质的信号和干扰，寻找最佳的处理方法降低噪声，一直是信号处理领域广泛讨论的重要问题。
目前有很多方法可用于信号降噪，如中值滤波，低通滤波，傅立叶变换等，但它们都滤掉了信号细节中的有用部分。
传统的信号去噪方法以信号的平稳性为前提，仅从时域或频域分别给出统计平均结果。
根据有效信号的时域或频域特性去除噪声，而不能同时兼顾信号在时域和频域的局部和全貌。
更多的实践证明，经典的方法基于傅里叶变换的滤波，并不能对非平稳信号进行有效的分析和处理，去噪效果已不能很好地满足工程应用发展的要求。
常用的硬阈值法则和软阈值法则采用设置高频小波系数为零的方法从信号中滤除噪声。
实践证明，这些小波阈值去噪方法具有近似优化特性，在非平稳信号领域中具有良好表现。
小波理论是在傅立叶变换和短时傅立叶变换的基础上发展起来的，它具有多分辨分析的特点，在时域和频域上都具有表征信号局部特征的能力，是信号时频分析的优良工具。
小波变换具有多分辨性、时频局部化特性及计算的快速性等属性，这使得小波变换在地球物理领域有着广泛的应用。
随着技术的发展，小波包分析(Wavelet Packet Analysis)方法产生并发展起来，小波包分析是小波分析的拓展，具有十分广泛的应用价值。
它能够为信号提供一种更加精细的分析方法，它将频带进行多层次划分，对离散小波变换没有细分的高频部分进一步分析，并能够根据被分析信号的特征，自适应选择相应的频带，使之与信号匹配，从而提高了时频分辨率。
小波包分析(wavelet packet analysis)能够为信号提供一种更加精细的分析方法，它将频带进行多层次划分，对小波分析没有细分的高频部分进一步分解，并能够根据被分析信号的特征，自适应地选择相应频带,使之与信号频谱相匹配，因而小波包具有更广泛的应用价值。
利用小波包分析进行信号降噪，一种直观而有效的小波包去噪方法就是直接对小波包分解系数取阈值，选择相关的滤波因子，利用保留下来的系数进行信号的重构，最终达到降噪的目的。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
中值滤波器脉冲噪声中英文资料外文翻译文献Improved 2-D Median Filter for On-Line Impulse Noise SuppressiomAbstract-An inproved 2-D median filter employing multishell concept to suppress impulse noise ,is presented.The performance of proposed filter is evaluated over image ‘LENA’,The impulsive noise is added using MATLAB utility.The modified strategy reduces the mnuber of replacement and results in better performance and simple hardware realization that is suitable for on-line implementation.Index terms-Median Filter , Multi-shell Median Filter, Impulse NoiseI.INTRODUCTIONIn TV and other imaging systems,impulse noise is a common impairment . The standard T.V.Broadcast signal is often contaminated with impulsive noise arising from various sources such as household electrical appliance and atmospheric disturbances.Broad banding of the signal further increases the level of impulsive noise. V arious filters are proposed to suppress such impairments[1].The median filter(MF)[1-2] is widely used for impulse noise suppression and the multishell median filter(MMF)[3] introduces the concept of missing line recovery. Although these filtershave satisfactory performance, MMF failsto filter two impulse noises in the same prossing window. Moveover,these filters tend to blur the images due to too many replacements. C.J.Juan proposed a modified multishell median filter (MMMF)[4], which removes most of the shortcomings associated with the MF and the MMF. However, it is observed that under certain condions, to be discussed in the follow sections, MMMF fails to perform the desired filtering operation .Moreover,the number of calculations/replacements invoved on the basis of MIN/MAX conditions is still too large and makes the filter difficult to realize,particulariy for real time applications.In this paper, the threshold strtegy of MMMF is modified so that:(a)effective noise filtering operations are performed under allconditions,and(b)number of calculations/replacements is reduced and simplified. This results in a simple hardware realization of the filter.II.PROPOSED MODIFICATIONConsider a 3x3-processing window, with P5 as the central pixel,as shown in Figure 1.P1 P2 P3P4 P5 P6P7 P8 P9Fig.1. A 3x3 processing windowThe output of MMMF as proposed in [4] isOutput (X,Y)= Max(P2,P8)if P5﹥Max[S]P5 if Min [s]﹤Max[S]Min(P2,P8) if P5﹤Max[S] (1)Where S is the set of samples surrounding central pixels except(P4.P6)i.e.S=｛P1,P2,P3,P7,P8,P9｝ (2) The principle invoved in the replacement strategy of Equation(1) is that if P5 is corrupted by noise ,it is better to replaceits gray level by P2 or P8 than by using Min[S] orMax[S] .also,due to missing lines error,since P4 and P6 may belost, they are not considered in Equation(2).The limitation of Equation(1) is that when Min[S] or Max[S] arealso corrupted by impulse noise,i.e.either Min[S] or Max[S] isequal to P5,Equation(1)fails to perform the desired filtering operation.To overcome this limitation following modificationsin the replacement strategy of Equation(1),are proposed.Output (X,Y)= Max(P2,P8)if P5≥Max[S]P5 if Min [s]＜P5＜Max[S]Min(P2,P8) if P5≤Max[S] (3)It has been observed that more than 70-80% points in an image,the gray level diatances of P5 from(P2 or P8) and from Max[S] are below 16.This is shown in Fig.2 for the image ‘LENA’.This fact is used to further reduce unnessary replacements,thereby reducing the bluring of the images.Thus taking into considertion of Figure(3) can be further modified asOutput (X,Y)= Max(P2,P8)if P5-Max[S]≥16Max(P2,P8) if Min [s]-P5≥16P5 otherwise （4）Equation 4 indicates that replacing action takes place only when the distance between P5 and Min[S] or Max[S] is no smaller than 16. This strtegy thus avoids the necessary replacements and reduces blurring of the images.Moreover，it can be implemented using simple comparators and subtractors.Gray level distancesFig.2. Gray level distances between central point and its neighboring points for the image ‘LENNA’Ⅲ .RESULTSFigure 3 shows the original image ‘LENNA’and Figure 4 shows the same image when corrupted with impulse noise. Results of median filter and the proposed filter are given in Figures 5 and 6, paring Figures 5 and 6, it is observed that the result of the proposed filter is much better than those obtained using the median filter. Aithough,the median filter remove the impulsive moise effectively, however,the image gets blurred.The proposed filter removes the impulsive noise and also preserves the details of the image.A multishell filter employing the modified replacementstrategy is presentde in this paper.The modified filter effectively suppresses the inpulse moise.It uses threshold conditions that require fewer comparisons and replacements and is faster as compared to the other multishell median filters.moreover,it can be realized using simple comparators and subtractors and subtractors and hence can be effectively used in real time applications改进二维中值滤波器在线脉冲噪声的抑制摘要：一种改进二维中值滤波器，采用多壳的概念，以抑制脉冲噪声，拟定的过滤器的性能进行评估超过图像“LENNA”的中值滤波，脉冲噪声被添加使用到MATLAB的实用工具中。