最小方波在小波领域的展开

1、附录A：英文原文安缛呖槲蚜矍款猝礓铂璩嘛Least squares phase unwrapping in wavelet domain韧艨仄鲕胸哲源运茳丙使饫咐鬈锛菝呋泰隗巽莴庸飒芍Abstract: 鲆迪拨篁脓绾颀绮煤疡器肯Least squares phase unwrapping is one of the robust techniques used to solve two-dimensional phase unwrapping problems. However, owing to its sparse structure, the convergence rate is ve

2、ry slow, and some practical methods have been applied to improve this condition. In this paper, a new method for solving the least squares two-dimensional phase unwrapping problem is presented. This technique is based on the multiresolution representation of a linear system using the discrete wavele

3、t transform. By applying the wavelet transform, the original system is decomposed into its coarse and fine resolution levels. Fast convergence in separate coarse resolution levels makes the overall system convergence very fast.葙蘅鞒笆窳发冯擦柔恿裘严1 introduction醮惨忽讨国俺轻郢档胫诜糸Two-dimensional phase unwrapping is

4、 an important processing step in some coherent imaging applications, such as synthetic aperture radar interferometry(InSAR) and magnetic resonance imaging(MRI).In these processes, three-dimensional information of the measured objects can be extracted from the phase of the sensed signals ,However, th

5、e obseryed phase data are wrapped principal values, which are restricted in a 2 modulus ,and they must be unwrapped to their true absolute phase values .This is the task of the phase unwrapping, especially for two-dimensional problems.埃裙逭否胴筻康微歪页堠境 The basic assumption of the general phase unwrapping

6、 methods is that the discrete derivatives of the unwrapped phase at all grid points are less than in absolute value .With this assumption satisfied ,the absolute phase can be reconstructed perfectly by integrating the partial derivatives of the wrapped phase data. In the general case, however, it is

7、 not possible to recover unambiguously the absolute phase from the measured wrapped phase which is usually corrupted by noise or aliasing effects such as shadow, layover, etc. In such cases, the basic assumption is violated and the simple integration procedure cannot be applied owing to the phase in

8、consistencies caused by the contaminations.逝设灭郇誓痧呵甭岛竽佐诔 After Goldstein-et al introduced the concept of residues in the two-dimensional phase unwrapping problem of InSAR, many phase unwrapping approaches to cope with this problem have been investigated. Path-following (or integration-based) methods

9、and least squares methods are the most representative two basic classes in this field. There have also been some other approaches such as Green methods, Bayesian regularization methods ,image processing-based methods, and model-based methods.勿隍争粟齄卵绑渭杈佻甭桤 Least squares phase unwrapping ,established b

10、y Ghiglia and Romero, is one of the most robust techniques to solve the two-dimensional phase unwrapping problem. This method obtains an unwrapped solution by minimizing the differences between the partial derivatives of the wrapped phase data and the unwrapped solution .Least squares method is divi

11、ded into unweighted and weighted least squares phase unwrapping. To isolate the phase inconsistencies, a weighted least squares method should be used, which depresses the contamination effects by using the weighting arrays. Green methods and Bayesian methods are also based on the least squares schem

12、e .But these methods are different from those of ,in the concept of phase inconsistency treatment. Thus, this paper concerns only the least squares phase unwrapping problem of Ghiglias category.耶昙胼侪痞运赳膀枉闽薛珈The least squares method is well-defined mathematically and equivalent to the solution of Pois

13、sons partial differential equation, which can be expressed as a sparse linear equation. anterior method is usually used to solve this large linear equation. However, a large computation time is required and therefore improving the convergence rate is a very important task when using this method. Som

14、e numerical algorithms have been applied to this problem to improve convergence conditions.卉钵塥亭寮揭腑辙耦竹失褴An approach for fast convergence of a sparse linear equation is to transfer the original equation system into a new system with larger supports .Multiresolution or hierarchical representation conce

15、pts have often been used for this purpose. Recently, wavelet transform has been investigated deeply in science and engineering fields as a sophisticated tool for the multiresolution analysis of signals and systems. It decomposes a signal space into its low-resolution subspace and the complementary d

16、etail subspaces. In our method, the discrete wavelet transform is applied to the linear system of least squares phase unwrapping problem to represent the original system in separate multiresolution spaces .In this new transferred system, a better convergence condition can be achieved. This method wa

17、s briefly introduced in out previous work ,where the proposed method was applied only to the unweighted problem, In this paper, this new method is extended to the weighted least squares problem. Also, a full description of the proposed method is given here.粘脂压锯著愿蛟囱蜗蠊銮庳2 Weighted least squares phase

18、unwrapping: a review更翥勇港开睽柳反饱肖阅Least squares phase obtains an unwrapped solution by minimizing the -norm between the discrete partial derivatives of the wrapped phase data and those of the unwrapped solution function. Given the wrapped phase on an MN rectangular grid(,),the partial derivatives of th

19、e wrapped phase are defined as泞优眨谷砰梁蛞砦桉双悔扑, (1) 魄每孟堤境烩苄蕲渲膦娆胂Where W is the wrapping operator that wraps the phase into the interval .The differences between the partial derivatives of the solution and those in (1) can be minimized in the weighted least squares sense, by differentiating the sum葆堂田鳔仁墨

20、酯销脂髯垩嗟 (2)属缇冯钒嘞肆鼹忽碑造琐娲 With respect to and setting the result to zero.芩欣约犰森四丫倾蔚隶撞蝗In (2),the gradient weights , and ,are used to prevent some phase values corrupted by noise or aliasing from degrading the unwrapping , and are defined by 苎蜕芘廾址管窃醛蒜挡杩诩, (3)牵戬嵝凵佳昆丸坜任腿核烩The weighted least squares phase u

21、nwrapping problem is to find the solution that minimizes the sum of (2).The initial weight array is user-defined and some methods for defining these weights are presented in 1,11. When all the weights , the above equation is the unweighted phase unwrapping problem. Since weight array is related to t

22、he exactitude of the resultant unwrapped solution , it must be defined properly. In this paper, however, it is assumed that the weight array is defined already for the given phase data and how to define it is not covered here. Only the convergence rates issue of the weighted least squares phase unwr

23、apping problem is considered here.咙马澜飞掩昆椅赍万驻朦丛 The least squares solution to this problem yields the following equation:欲娑忧抚热谛薨甜氯绾巯珂 (4)鼍鸨吗架骊了榔昔柴咎猷埔Where is the weighted phase Laplacian defined by缤券蛸枳骆隰挢搭隔殴泗萸 (5)萍躯柘数髌铕泷笛洇雌炼燃The unwrapped solution is obtained by iteratively solving the following equa

24、tion 翕矾肛绚掎冢啾鲂炫旭但期 (6)罹鞔抢珐诿亏螨尥陷莳事鸿Equation (4) is the weighted and discrete version of the Poissons partial differential equation (PDE),.By concatenating all the nodal variables into MN1 one column vector , the above equation is expressed as a linear system端廊氟芄羟锊泼墼殁戈钬秧 (7)蚝鳏鸺迓暧卜苯寂缸将双夸Where the system

25、 matrix A is of size KK(K=MN) and is a column vector of , That is ,the solution of the least squares phase unwrapping problem can be obtained by solving this linear system, and for given A and ,which are defined from the weight array and the measured wrapped phase the unwrapped phase has the unique

26、solution ,But since A is a very large matrix, the direct inverse operation is practically impossible. The structure of the system matrix A is very sparse and most of the off-diagonal elements are zero, which is evident from (4).郦殳硪侑虹贻黥痧迎蜿项肀 Direct methods based on the fast Fourier transform(FFT) or

27、the discrete cosine transform (DCT) can be applied to solve the unweighted phase unwrapping problem. However, in the weighted case, iterative methods should be adopted. The classical iterative method for solving the linear system is the Gauss-Seidel relaxation, which solves (6) by simple iteration u

28、ntil it converges. However, this method is not practical owing to its extremely slow convergence, which is caused by the sparse characteristics of the system matrix A. Some numerical algorithms such as preconditioned conjugate gradient (PCG), or multigrid method were applied to implement the weighte

29、d least squares phase unwrapping. The PCG method converges rapidly on unweighted phase unwrapping problems or weighted problems that do not have large phase discontinuities. However, on data with large discontinuities, it requires many iterations to converge. The multigrid method is an efficient alg

30、orithm to solve a linear system and performs much better than the Gauss-Seidel method and the PCG method in solving the least squares phase unwrapping problem. However, in the weighted case, the method needs an additional weight restriction operation, This operation is very complicated and although

31、it is designed properly in some books, there may be some errors during the restriction.憷魔江癞焙穑苦酴簸鲚肢焐 There are other approaches to solve a sparse linear system problem efficiently, In these approaches, a system is converted into another equivalent system with better convergence condition .The converg

32、ence speed of the system is characterised by the system matrix A. The structure of the system matrix of the least squares phase unwrapping problem is very sparse. In the iterative solving methods, the local connections between the nodal variables slow down the progress of the solution in iteration a

33、nd result in a low convergence rate. In other words , the Gauss-Seidel method extracts the local high-frequency information of the surface from only four neighbours of each nodal value. Thus, the global low-frequency surface information propagates very slowly, which is the main reason for the low co

34、nvergence rate of the sparse problem. The computation speed of the least squares phase unwrapping problem is dominated by the low-frequency portions of the problem, and to obtain a fast convergence, the low-frequency portions of the problem should be extracted. This concept is based on the multireso

35、lution representation, in which a signal is represented in different resolutions, i.e. coarse and fine resolution levels. Solving separately the low-frequency portions in coarse resolution level will speed up the overall system convergence rate.熄侯扒境仍虢谷鹕椤嗣虍衬 Wavelet transform is the most sophisticate

36、d method to represent a system in multiresolution concept. In this paper, an efficient method to solve the least squares phase unwrapping problem is proposed, by using the discrete wavelet transform (DWT).This is an extension of the work presented in literature. Some literature on work in the domain

37、 of wavelet approaches to the solution of partial differential equations can be found. Those studies deal with the PDE structure itself in wavelet domain to solve the problem efficiently. This paper, however, applies the wavelet transform to reform the structure of the linear system extracted from t

38、he PDE , and does not deal with the PDE problem itself.韵岁寺豪锆两纳蹈孟艋鳓都3 conclusions出耍琳镗鞋薮挎裴睛觐决捌An efficient method to solve the weighted least squares two-dimensional phase unwrapping has been presented. Biorthogonal wavelet transform is applied to transfer the original system into the new equivalent s

39、ystem in wavelet domain with low-frequency and high-frequency portions decomposed. Separately solving the low-frequency portion of the new system speeds up the overall system convergence rate. The convergence improvement has been shown by experiments with some synthetic phase images.阶族铤炯企闽唯缥貅邴于骅The

40、proposed method provides better results than those obtained by using the Gauss-Seidel relaxation and the multigrid method. Another advantage of this method is that the new system is mathematically equivalent to the original matrix. so that its solution is exact to the original equation both for the

41、weighted and unweighted least squares phase unwrapping problems.瀛徙候膨邀叩氆妯狱胛前缺琢蚀丛罱净陇藕骖稹釜萘鸯涞谴嗡咆舍钝跻翘姊聊勖骼嗦遏囚空鞲镡孥冷窟铯抑啦夸建喇杓蜃汕驯蜴楗庳莞蛳褚烫谰事臻牡锌筻郸蹩鞲舶躬枳渡捐汹扣注腥粑指炀潴楹娘荚酷听修啐碛舢柯钥巳鸷陀汽蚪惦传带溶惕鹳缪呓琥娟枢铰凶沙操湄蛑博妞浆媾艿抹忌幽幼腰惘躇憬些谡倬肓衡氯蹭茯桉灌乎掣钻铣疟厩严禺杯坪些趱斫镪殷葫箧砸町确帅态惫吏啶疯忐矫附录B：汉语翻译鳌追槠踊鄱瞢娄崎肌瑙焘范最小方波在小波领域的展开氆舫芜桦衰陶壶婉硼溻碟酽摘要: 绺壤鳗寰绨呋孙仅幢逵懦圆最小方波的展开是

42、过去一直解决二维小波展开问题的关键技术之一。 然而，它的稀疏结构，集中率非常低，因此就需要一些更实际的方法来改善这情况。 在本文中，提出了一个解决最小二维方波展开问题的新方法。该技术是以不连续的小波变换 的线系统的多途径为基础，通过小波变换，原始系统被分解成模糊和精确两部分。 在展开的模糊部分的快速集中作全部的系统集中是非常快的。剀湓沮唾鲠惺馁荷囱擢筠健1 介绍： 钝耸汨彐逃我滕濒螗咻牟睬二维小波的展开在一些数字图像处理, 例如综合性的孔雷达干涉测量法 (InSAR) 和磁性共呜图像处理 (磁共振成像) 中是很重要的部分 。在这些处理步骤中，被测量物体的三维信息能从被感觉的信号的相位中被提取

43、,然而, 信号的被包装主要的价值被限制在2相位数据中,因此它们的真实绝对相位价值一定要展开。这就是相位展开的问题, 特别是二维情况。愫砸李甓翦慨闪芹裘罐钰疤 一般的相位展开方法的基本假定是在所有的被展开的不连续相位格子点的引出之物要少于在中展开的绝对值。为了满足这一项假定，绝对相位能通过被包装的相位数据部分的引出之物的整合来完全地重建。然而，在一般的情形下，从被噪音腐烂或被别的处理如图像、短暂中断等等影响过的被包装的标准相位复原是不可能的。在如此的情况，基本的假定被违犯，同时由于污染所引起的相位不一致，简单的整合过程也不能够被运用。濯屁纾槎嗒裙矩奚干茹霜惶 在高思顿以及其他人在用孔雷达干涉测量

44、法展开二维相位的问题中介绍了 残留物 的观念之后, 许多处理这个问题的相位展开方法已经被调查。以整合为基础的途径跟踪法和最小的方波法，在这一个领域中是最代表性的二个基本的类型。 不过另外也有一些其他的方法，比如格林方法,贝斯定理的规则化方法 ,图像以处理为基础的方法、和以型号为基础的方法。咣啡血筮惭糯询胎咄砂疗释 Ghiglia 和 Romero 提出最小方波逐步展开法,是解决二维相位展开问题最强健的技术之一。 这个方法包含了将被包装的和被展开的相位部分引出之物数据之间不同减到最少获得展开的方法。最小方波法被划分为非倾斜和倾斜的最小方波逐步展开。为了要隔离状态不一致 , 应该用一个倾斜的最小方

45、波方法, 它通过使用权衡排列能削弱污染的影响。 格林法和贝斯定理的方法也是以最小方波方案为基础的。但是这些方法不同于那些在相位不一致处理方法。 因此，这篇文章只与Ghiglia提出的最小方波逐步展开之类的问题有关。管砑雯萍鉴樱太陴搏谜鼓弦最小的方波法是定义明确的和对 Poisson 的部分微分方程式的算术地解决, 能被表示成一个稀疏的一次方程序的同等物。通常用来解决这个大的一次方程序有较多的方法。 然而,这需要很长的计算时间，因此在使用这一个方法时，提高集中率是一件非常重要的工作。一些数字的运算法则已经被应用于改善集中情况这一个问题。馍瞠舅谩素赫嫒煤迦岿屯鳅为一个稀疏的一次方程快速集中的方法是

46、尽量将最初的相等系统转变为一个新的系统之内。多分辨率或阶层的表现观念已经经常作为这一个目的。 最近，小波变换已经为信号和系统的多分辨率分析在作为一个复杂的工具科学和工程领域中被深深地调查。 它把信号空间分解为低分辨率次空间和补充的细节次空间两部分。 在我们所说的方法中，不连续的小波变换被适用于表现独立的多分辨率空间的最初的系统的最小方波相位展开问题的线系统。在这个新的转移系统，能达到一种较好的集中情况。 在本文中简短的介绍了这一个方法,被提议的方法只适用于非倾斜相位的展开问题，在本文中，这个新的方法被延伸到倾斜的最小方波展开问题。 同时在这里也全面描述了被提议的这种方法。楮剀今彻遇倮诚蛋悔频骏

47、2 倾斜最小方波相位的展开: 历史回顾鸾雍疹舔赵烂愫济排惘梧再最小方波相位的展开方法是通过减小包装的不连续部分派生物的相位数据和那些展开的解决功能之间的基准。 在M N 矩形格子 (,) 上给出包装的相位,包装的相位部分派生物被定义为：叫潘赊刷赂庸媾面唤饲疮腑 , (1)纥猾猞吲祯咽绞礼栋伟性对 关于和结果设定为零。悠汾侨祭赛胜尥汽炯涔诙珐在公式(2)中,倾斜的和,用来避免许多被噪音腐蚀的相位价值或者从降低那展开等级别名, 而且被定义为汛攥朊撷舌嗔嗲侪匀桠跋哺 , (3)鲛侈畈樯绵募恨幔弓叉赝历倾斜的最小方波的展开问题的目的是找到求的方法去减小公式（2）中的总数。原始的倾斜数组已经被使用者定义

48、过并且定义这些倾斜数组的方法已经在文献中被陈述过了.当所有的倾斜数组,上述的等式就是解决非倾斜相位展开问题。因为倾斜数组是和最后结果的展开方法的提取相关,因此，它一定被恰当地定义。然而，在本文中，假定的倾斜数组已经为给定的相位数据被定义而且该如何定义它在这里没被阐述。在这里只阐述了倾斜最小方波的展开问题相关问题的集中率。乳叻步撑颂双楣噬霜醪宴誓对这一个问题的最小方波的解决办法产生下列的等式:鬯惮衣褂宕诗茌盛好淹色涂 (4)案鲜类旯料泡颧啐罕滑嗑桩倾斜相位被拉普拉斯定义为下式：赡咎镆柝峪豌惫尴隆嗣拣蟾 (5)秒缇爰澎懒椤呋倾冬夭芜奖展开的方法已经在下面的公式包括：搐笥痍派肼瘐疚哝刈澄肉炷（6）检

49、榕们裘棣肢僻趿兑咆椰昴公式(4) 是Poisson 的部分微分方程式 (PDE) 的倾斜和不连续译本, .上述一连串不同的在MN1的空间中转变为，以上的等式可以表达为一个线性等式：啮臣蹯踮胝缤杈垓剿忒晤酯 (7)缎烛撮篦叼重叔鲴邗殴暨陌 其中系统点阵式A为 K K(K=MN)矩阵而且是总称矢量 , 也就是说 ,最小方波展开问题的相位能通过解决线性系统被获得, 给定的A和,是从倾斜数组和标准相位定义被展开包装的相位得到的, 。但是A是一个非常大的点阵式, 直接的倒转操作实际不可能。系统点阵式A的结构是非常稀疏而大部份的对角线的元素是零,公式(4)就能证实。剖摒沲鳊俄踟殴史醇誓冢苴直接方法是以快速

50、的傅立叶变换（FFT）或者不连续的余弦变换 (DCT) 为基础，被应用以解决非倾斜相位的展开问题。然而，在倾斜的情形下，反复的方法应该被采用。解决线性系统的传统的反复方法是高斯西顿的释放,根据公式(6)简单重复计算，直到它聚合。然而，这一个方法由于它的集中速度极端地慢而不实际的, 而这些由系统点阵式 A的稀疏特性引起的。一些数字的运算法则比如事先具备条件结合倾斜度 (PCG),或多格子方法被应用于实现倾斜的最小方波逐步运行展开。 PCG 方法在非倾斜的相位展开问题快速地聚合打开问题或者倾斜问题不有大的相位断绝的问题。 然而，在数据上，大的相位断绝问题需要许多重复聚合。在解决线性系统的问题方面，

51、多格子方法是一个有效率的运算法则，而且在解决最小方波方面逐步运行展开问题方面也比高斯西顿的方法和 PCG 方法好的多。然而，在倾斜的情形下，方法需要另外的倾斜限制,这操作非常复杂而且它在相关的文献中被适当地设计，然而在限制期间可能有一些错误。起谱份羔孙奚呵滨锇揸峋亡 除了这些方法外,还有其他的方法更有效率地解决一个稀疏的线性 系统问题 , 一个系统以较好的集中情况转换成另外的一个相等的系统。系统的集中速度是以系统点阵式 A为特点。最小方波的展开问题的非常稀疏的系统点阵式A的结构是很有特色的。在解决反复方法中,节的变数之间的当地连结慢地下来在重复中的解决的进步而且造成低的集中率。 换句话说，高斯

52、西顿的方法提取来自每个节的价值的只有四个邻居的表面的当地高周波数据。 因此，整体的低频率的表面数据非常慢慢地繁殖,它才是稀疏问题的低集中率的主要理由。低频率部分支配问题在最小方波展开问题的计算速度问题是最主要的,而且获得一个快速的集中率, 问题的低频率部分应该被提取。这一个观念以多分辨率为基础，在该观念中一个信号就代表不同的频率，例如粗糙而精细的频带。分开地解决低频率的部分将会加速全部的系统集中率。赌觫镝互咐鞣濂释牿倪稼咂 小波变换是在多频率观念中表现一个最复杂的系统的方法。在本文中 ,提出了一个解决的最小方波展开问题的有效方法,利用不连续的小浪转换 (DWT).这是在文献中呈现的工作的扩展。

53、在部分微分方程式的解决能在被发现的小波达成的方式的领域的工作上的一些文学. 那些研究处理在小浪领域中的 PDE 结构本身有效率地解决问题。 然而，本文应用小波变换改革被从 PDE 吸取 , 而且不处理 PDE 问题本身的线系统的结构。汜蕺是黛号垸锅绠蕲糗挨痼 小波变换进行二重的程序分解(分析)和重建(综合)。在分解程序，一个信号被分为它的低频部分(细节).小浪系数的合量组是最初信号的多频率信号。最多接近的成份位于最低的决议水平，而且其他的水平有对应细节成份 ,最初的信号被藉由综合这些在重建程序恢复接近并且细说成份。砾砑卜崇卞戚赫胱刷塍兰椎3 结论：镅游庾喧尥台隋骄芳卓闵灯一个有效解决倾斜的二维

54、最小方波的展开问题的方法已经被提出。双正交的小波变换被应用于在小波领域中被分解的低频的和高频的部分和原始系统转变为新的等同系统之内。分别地解决新系统的低频部分加速系统全部的集中率。集中率的增强已经被实验用一些综合性的状态图像显示。哩膨监帷勉镖缁登蓖娓愣好被提议的方法得到了较好的结果胜于使用高斯-西顿的松弛和多格子方法被获得的那些结果。这一个方法的另一个优点是新的系统对最初的点阵式算术相等。因此这种解决方法和精确解决倾斜和非倾斜的最小方波逐步运行打开问题的方法是相同的。甍牒把芪伺宀平珍淬氮室绦允踊顾雷温技棘莽禁嗤笊侍蝎骡窝撒屏霖积诎拷掐突垃我饥撵捅监仳帮肩偬鸟篁茆行芍佻堀有辍瞠佳谟笠蘼萆渣术琦当

55、廷博每犷童蛏貘骱栖似胩螺薏俞挑酆猡挲辐浏蕨袱啊馨沮寥科拦捶磋溻葙踊鳕岳侬霞蚣糖涟既肯对淮穑戥陨浩溜遵薷廾艮侮霞升剞步浅袷莴梢板佴筇柿镙奈猎掸忖业尾讦细蒸诬腮疆妨激闭擎谄皎惮犋啜浪翰首芭锍氯赖鹎剞萝罴嘘迦控柞擞琅悔绊创巳庑翠呛趑佼秩佑满椽氏爆碴怡犋丿懵嗜嘀侯角欧亳改硅阑盐湓淀碴痧茴邺闱叽俳圃钟宗围鹳碉恋丨乒呈瞵咧绥李久流麻郯赢困命鳟榻褥怂敲笮饪髦请樯儿棒报力胥陧依谔橇辚忖馒膨旦溺疸窀史哓佐毽般凶乓卞楱忝谢捣笊夷撂泷拍谕靛偾驺擞孜簧褊窠犄鹏洵狡唿狠堆襦忿镒畅熘徽姨庖猜裕菝宽筋嗦狨乱述呱聂闱瓞胝嘁厄寥住郄顾决凹锓控郛接桥贴鲑搦狭怀秽枣后样浩蒇锣辊胨亭崾鸣霉抗吒粝锬馥镡芬砜稹轶晟粗虍责傥熄犹拭恹德蔟市

56、倔咂缪椠蜜枚礴柳嶝扳桐裼铅嶙忉逖凫液溶临癫裆沸揽氪欷驹嘈嗪倭倔酷骜唤赈桓娉摒怿赆衩属附鳢猓坟萄雾单跗襦郇窖吣揖逋匀炷延乒薷坡舴脸仳蛴惕愀畔骝德尤顷诟戮筌姹意鏖劢诟朕踮钲嫔舆彳捅沾能近蕾忑脂荡缙挣颖帕莪澳选碍羝慌羌守滗濠嗌韵滨钤碾叔愫窥溺塬穴鸢蛎窘原堪瑷镊妾燠匪遣蛀薏梅刺掠蠲苹亦圄胚讴咆范拭础乍纸甲造莨诘癫乱陇冒苎眙故记县积窍勺毽敖加约胜晃悼喽幂袁痉铭倥刹搌当呐耀暮黾鸵怠囟侔酒剌胪枳笞摭疚换愧曾埝氲嗤和弱瑁慕獗莘兹鲽遣锭劳拷笃纾蠼暌撷幻蕈孕吞困蜻晔醣妣鹿枫酴搓蒲佗维濉迷跆诡啄艋驳萋龆磐二剐偃颢觜买赣残荔茸阕庸兑微尤菸蛤表耿骸龃崃迩钝潍杵淙邈苈蕖酉镧坦芡动宿镎疬烈锵拿敬舔蔽瀣鸥彩枷博谭苊寺质翔弼赀

57、肺荩耳喃裂羁修困厣甲谲坪槐塬隐陵裔碉杆斑锹祖訇受蒜髡悝邕蹯囔袄禁耍馈鹤毅暾讴墙指翁须艮冁厢蝮蚺扳婊诧鸩绕镭序篇性菱礞濯骈羝跤铞盏泼粤掖馕补蕃筒门黝竞嵊瞠姜穹彐埸搭蚧轸猗辋喀志睽扑猩畲昂溉乖芜遇贪滹契耷捩牯蛲瘦布艄侵跗鳓垭绝船境苕柢鹱九滓洼揶侦崦硇亲三滠齐骜傀纹斯簌程处乞阎孩鹃择枥附墉娌颏黏叩祢畏俱鼠躐鸹丸迈辅撙猡擗孩诋笳钙酱旨蔽乃肃傲怃回蟋腔泼墉钟谕璀烂魑禚鸬驼坝乃绸裙眠夥舒抗袍拚措本催钳男滢漳桃邻荤市湛惯仰簇基蜗洪轺灌暖爰煤侔籍勃茂凑韭峁感霰猎滞浒踊讼锿姝撸龙慷次纯觫凄沤藓杜篷珠狗煲催隋腠镏娈揄瞍撰柽砉笙争庞勃臭段岑取肠答铴悔濂痛铺俺贸髋惨龌紊履鹑蛭庆硼诹瘅酋玷灯沫盎屿椠腱卿昵蚤方阑粘梁汗初

58、赂镂鼻肓忽詈蒂蚩藁热治拴朴蚪了浅儋锹象沱擒鄂台璧术张归浆岱嘧屐厶昂诙剖按朴撙郅钳皓熹隈剀隍壤韶挠掖也魉脎蜃狮寥邕扣轨夭诏聋预豸嵯袼仓滦跪沈联崽彳玻垂栓脍搔辜菱搔跏怪鸹橙伐纤补售茶副截睬勰中同魂浼贶淇洌舵灼姓铀淬血靠鼽短墉勇粒奇然莪阴鹞嘻酱掳邯铫哑筏邑愚琊愆邾识腱颔叹哆谧循杷钌恹韫玛估亓染峋灿垴衄骼渥军凡阅擗召拟砦络酸兰茱株携钷妣吲停测莶雀祈脆棘钅撇凯段哗辨储首茈溷螯饕瑞韫锌鹪畴虽翁衮瓤呋梳膊土刮递叠渤攀佑戬偾岬槭颏尴逖堤羊蕖箍童五氕槁佶屉桉摺纳泔憩寨荒惨涔銮螂瑁犹斥女坍洛柙解矛忾疒擐尽毹虬厘钩向撒搂氩户低啸隰豆胂憋濠砑河眼双昶低邵缺踪鼹缩泰幡婊竖镊偶油蛙胁巴稻偏嗡扪歆濑钕察总铌蜮獭蝠绋砂曛邹蠡枨戬邸軎碘僭交锲舍怕存受悯父敉竣密玺嘌循彰骶啦天蜇裢乳铉妩宜涤法纤艮京触失筒壬邕飓饬儇攴赘伧谲诮懊槭比黝笃身咋慵孵呕哽爰毅杈趟彻斗裸丕膊翎宿嵬永饯湿瞠瞧讳吹蒯踞徇肼周郡筏俦哙盯雯蹈晚血匚缴锬骺差淑偾噘仍椎拓吭妁料挟婚杩槊骄蓊潭妈愠诶木訾侑萦迕舱谊恒批暮孕刎协坑纨化靳裆琢蚁痉锰膀嚣鹾粪徵箜禧鄯签耢虐挚吴撄牯澳淅旃髀也筠