当前位置：文档之家 › Estimation of High Resolution Images and Registration Parameters from Low Resolution Observ

Estimation of High Resolution Images and Registration Parameters from Low Resolution Observ

  • 格式：pdf
  • 大小：176.93 KB
  • 文档页数：8

Estimation of High Resolution Imagesand Registration Parametersfrom Low Resolution ObservationsSalvador Villena1,Javier Abad2,Rafael Molina2,and Aggelos K.Katsaggelos31Dpto.de Lenguajes y Sistemas Inform´a ticos,Universidad de Granada,18071Granada,Spainsvillena@ugr.es2Dpto.de Ciencias de la Computaci´o n e I.A.,Universidad de Granada,18071Granada,Spain{abad,rms}@decsai.ugr.es3Dept.of Electrical and Computer Engineering,Northwestern University,Evanston,IL60208-3118aggk@Abstract.In this paper we consider the problem of reconstructing ahigh resolution image from a set of undersampled and degraded frames,all of them obtained from high resolution images with unknown shiftingdisplacements between them.We derive an iterative method to estimatethe unknown shifts and the high resolution image given the low resolutionobservations.Finally,the proposed method is tested on real images.1IntroductionHigh resolution images can,in some cases,be obtained directly from high preci-sion optics and charge coupled devices(CCDs).However,due to hardware and cost limitations,imaging systems often provide us with only multiple low reso-lution images.In addition,there is a lower limit as to how small each CCD can be,due to the presence of shot noise[1]and the fact that the associated signal to noise ratio(SNR)is proportional to the size of the detector[8].Over the last two decades research has been devoted to the problem of re-constructing a high resolution image from multiple shifted undersampled,and degraded frames(see[4]for a recent review).A key part of the high resolution reconstruction problem is the estimation of the shifts between the images.Most of the reported approaches on shift estimation for super-resolution,first estimate the displacement vectors either by interpolating the low resolu-tion observations and thenfinding the registration parameters or byfinding the low resolution registration parameters in the low resolution domain and then interpolating them(consider[4]again),where the high resolution image and the registration parameters are estimated simultaneously,can be found in[10,9,12, 2].This work has been partially supported by the“Comisi´o n Nacional de Ciencia y Tecnolog´ıa”under contract TIC2003-00880.A.Sanfeliu et al.(Eds.):CIARP2004,LNCS3287,pp.509–516,2004.c Springer-Verlag Berlin Heidelberg2004510Salvador Villena et al.In this paper,starting from the low to high resolution method described in [6,11],we propose a new iterative method to alternatively estimate the registration parameters and to obtain a high resolution image from a set of low resolution observations.The paper is organized as follows.The problem formulation is described in section 2.The process to estimate the displacements between the images is described in section 3.Once the registration parameters have been obtained the application of the Bayesian paradigm to calculate the MAP high resolution image and estimate the regularization hyperparameters is described in section 4.The iterative estimation of the registration parameters and the high resolution image is discussed in section 5.Experimental results are described in section 6.Finally,section 7concludes the paper.2Problem FormulationConsider a set g ={g 1,...,g Q }of Q ≥1low resolution images with N 1×N 2pixels.The low resolution sensors obtaining the low resolution images are shifted with unknown horizontal and vertical displacements with respect to each other by values proportional to (T 1/L )×(T 2/L ),where T 1×T 2is the size of each low resolution sensing element and L denotes the high resolution magnification factor.Our aim is to reconstruct an M 1×M 2high resolution image,f ,with M 1=L ×N 1and M 2=L ×N 2,from the set of low resolution observations.We now relate the low resolution observed images in g to the unknown high resolution image f we want to estimate.Since the low resolution sensors obtain-ing the low resolution images are shifted with unknown horizontal and vertical displacements with respect to each other by values proportional to T 1/L ×T 2/L we have that each g i ,i =1,...,Q results from f through motion compensation,filtering,and subsampling:g q =AHC (d q )f + q q =1,...,Q ,(1)where C (d q )is the (M 1×M 2)×(M 1×M 2)matrix defined by C (d q )f (a,b )=f (a +d x q ,b +d y q )),where d q =(d x q ,d y q )denotes horizontal and vertical displace-ments,matrix H of size (N 1×N 2)×(LN 1×LN 2)describes a high resolution filtering operation,A is the downsampling matrix with size N 1N 2×(M 1M 2)and q a random independent noise with variance β−1q .Matrices A and H are assumed to be known.AH models optical distortion together with CCD pixel resolution [11].The matrix H is assumed to be block-circulant and C (d q )will be approximated by a block-circulant matrix as well.Our problem can now be formulated as the estimation of the set of displace-ments,d ={d 1,...,d q },and the high resolution image f .In order to perform this task we will proceed in two steps;first we will estimate the displacements d and then the high resolution image f .Estimation of High Resolution Images 5113Estimation of the Displacement VectorsTo obtain the high resolution image f ,we need to accurate estimate the pixel displacement d q in (1).In this paper we estimate the registration parameters as follows.From (1),we first consider for each q ∈{1,...,Q },the following low to high resolution observation model with just one observation,g q =AHu q +ψq q =1,...,Q ,(2)where u q is an M 1×M 2high resolution image,H and A have been defined in (1)and ψq is random independent noise with variance µ−1q .We then use as prior model for u q a simultaneous autoregression (SAR)[7],that is,p (u q |αq )=1Z prior (αq )exp −12αq C u q 2 ,(3)where theparameter αq measures the smoothness of the ‘true’image,Z prior (αq )=( i,j λ2ij )−1/2(2π/αq )(M 1×M 2)/2and λij =1−2φ(cos(2πi/M 1)+cos(2πj/M 2)),i =1,2,...,M 1,j =1,2,...,M 2and C is the Laplacian.Then we select as estimate of u q ,ˆu q =arg min u qαq C u q 2+µq g q −AHu q 2 (4)The high resolution estimate ˆuq is found together with estimates of αq and µq using the method described in [6,11,5].One of the ˆuq ’s (call it ˆu )is an initial estimate of f ,and we proceed with the estimation of the registration parameters d ={d 1,...,d q }.In this paper we have used the following two approaches.3.1Method I:Global CorrelationGiven ˆuq and the initial estimate,ˆu ,of the high resolution image f we calculate the registration parameters between ˆuq and ˆu by finding (ˆd x q ,ˆd y q )=arg max d x q ,d y q C ˆu q ,ˆu (d x q ,d y q ),(5)where C ˆu q ,ˆu (d x q ,d y q )= n 1n 2ˆu q (n 1,n 2)ˆu (n 1+d x q ,n 2+d y q )(6)3.2Method II:Local CorrelationGiven ˆuq and ˆu we compute a set of points of interest in image ˆu q (see for instance [3]).Then,for each pixel location (n x ,n y ),corresponding to a point of interest,consider a block of small size centered around it and search for the location of the best-matching block of the same size in a limited area aroundpixel (n x ,n y )in image ˆu.512Salvador Villena et al.Let d q(i)for i=1,...,points of interest be the set of obtained registration parameters;we then create the list L={d n q,n=1,...,N},of the N most frequently appearing values of the registration parameters(N should cover a high percentage of the points of interest).For each d n qfind the correlation between the central part ofˆu q of size M1/2×M2/2and the same area in imageˆu displaced by d n q.Select the registration parameter maximizing this correlation.4Bayesian AnalysisOnce we know the registration vector d,from(1),the probability density func-tion of g q,with f the‘true’high resolution image,is given byp(g q|f,βq)=1Z(βq)exp−βq2g q−W q f 2,(7)where Z(βq)=(2π/βq)(N1×N2)/2,βq is the inverse of the noise variance and W q=AHC(d q).Since we have multiple low resolution images,the probability density function of g given f isp(g|f,β)=qp(g q|f,βq)=1Z noise(β)exp−12qβq g q−W q f 2(8)whereβ=(β1,...,βQ)and Z noise(β)=qZ(βq).As prior model for f we use the simultaneous autoregression(SAR)defined in(3),that isp(f|α)=1Z prior(α)exp−12α C f 2,(9)Note that this SAR model is equivalent to the use of the Laplacian operator to regularize the high resolution image estimate.The Bayesian analysis is performed to estimate the hyperparameters,αand β,and the high resolution image.In this paper we use the following two steps: Step I:Estimation of the Hyperparametersˆαandˆβarefirst selected asˆα,ˆβ=arg maxα,βL g(α,β)=arg maxα,βlog p(g|α,β),(10)where p(g|α,β)=fp(f|α)p(g|f,β)d f.The solution to this equation is obtained with the EM-algorithm with X t= (f t,g t)and Y=g=[0I]t X.Estimation of High Resolution Images 513Step II:Estimation of the High Resolution ImageOnce the hyperparameters have been estimated,the estimation of the high resolution image,f (ˆα,ˆβ),is selected to minimize ˆα Cf 2+q ˆβq g q −W q f 2,(11)which results inf (ˆα,ˆβ)=Q ˆα,ˆβ −1 q ˆβq W t qg q ,(12)where Q (ˆα,ˆβ)=ˆαC t C + q ˆβq W t q W q .The estimation process we are using could be performed within the so called hierarchical Bayesian approach (see [6])by including priors on the unknown parameters αand β.Note that the prior model in (9)plays an important role in the estimation of the high resolution image and the hyperparameters when an incomplete set ( q βq W t q W q singular)of low resolution images is used [5].Note also that equation (12)can be solved using the method proposed in [6].5Iterative Registration and Image EstimationCombining the methods proposed in section 3to estimate the registration param-eters,d ,and the algorithm to estimate the high resolution image,f ,described in section 4,we can obtain an iterative procedure to estimate both of them.Let d 0be the displacement vector obtained by applying the methods de-scribed in section 3and f 0the image estimate obtained by the method described in section 4when d =d 0.Then re-estimate the displacement vector d k by us-ing ˆuq obtained in section 3and replacing ˆu (the initial estimate of the high resolution image obtained in section 3),by f k −1.Recalculate f k for this new displacement vector d k using the method described in section 4.The iterative procedure ends when f k −f k −1 2/ f k −1 2is less than a prescribed bound.6Experimental ResultsA number of experiments were performed with the proposed algorithms over a set of images to evaluate their performance to estimate the high resolution image and registration parameters.According to (1)the high resolution image in Fig.1a,f ,was first shifted to obtain a set of 16high resolution images,f l 1,l 2(x,y )=f (x +l 1,y +l 2),(x,y )∈{0,...,M 1}×{0,...,M 2},l 1,l 2=0,...,3.Then each f l 1,l 2was blurred using a motion blur of length 10and downsampled by a factor of four.Gaussian noise was added to each low resolution image to obtain three sets of sixteen low resolution images,g l 1,l 2,with signal to noise ratio (SNR)equal to 20,30and 40dB.514Salvador Villena et al.Table1.Estimated registration parame-ters for the low resolution image set with SNR of20dB,by global correlation(first table)and local correlation(second table) Global correlation.Estimated shifts in table Real shifts01230(0,0)(0,0)(0,0)(0,0)1(0,0)(0,0)(0,0)(0,0)2(0,0)(0,0)(0,0)(0,0)3(2,0)(1,0)(1,0)(1,0)Local correlation.Estimated shifts in table Real shifts01230(0,0)(0,1)(0,2)(0,3)1(1,0)(1,1)(1,2)(1,3)2(2,0)(2,1)(2,2)(2,3)3(3,0)(3,1)(3,2)(3,3)Table2.Estimated registration parame-ters for the low resolution image set with SNR of30dB,by global correlation(first table)and local correlation(second table) Global correlation.Estimated shifts in table Real shifts01230(0,0)(0,0)(0,0)(0,0)1(0,0)(0,0)(0,0)(0,0)2(0,0)(0,0)(0,0)(0,0)3(1,0)(1,0)(1,0)(1,0)Local correlation.Estimated shifts in table Real shifts01230(0,0)(0,1)(0,2)(0,3)1(1,0)(1,1)(1,2)(1,3)2(2,0)(2,1)(2,2)(2,3)3(3,0)(3,1)(3,2)(3,3)In order to test the performance of the proposed algorithms we ran the reg-istration algorithms in section3and the reconstruction method described in section4on different sets of q randomly chosen low resolution images with 1≤q≤16.For comparison purposes,Fig.1b depicts the zero-order hold upsampled image of g0,0for30dB SNR(PSNR=13.68dB)while the bilinear interpolation of g0,0is shown Fig.1c(PSNR=14.22dB).Tables1and2show,at convergence of the method described in section5, the estimated displacement vectors obtained using the methods described in sections3.1and3.2for the20dB and30dB cases,respectively.The estimated registration parameters did not change between thefirst and second iteration of the method.The estimated high resolution image for the30dB case using local correlation and6low resolution images is depicted in Fig.1d(PSNR=21.03dB).7ConclusionsIn this paper we have presented an iterative method to estimate the registration parameters and reconstruct a high resolution image from a set of shifted low resolution observation.The method has been tested on synthetic images and has provided good estimates of both the high resolution image and displacement vectors.The proposed method works for the case of shift displacements between the high resolution images.Extension to homographies,of particular interest for plane surfaces like registration plates or text,is under study.Estimation of High Resolution Images 515(a)(b)(c)(d)Fig.1.(a)original image (b)zero order hold,(c)bilinear interpolation,(d)image obtained with the proposed method using 6low resolution imagesReferences1.K.Aizawa,T.Komatsu,and T.Saito.A scheme for acquiring very high resolution images using multiple cameras.In IEEE Conference on Audio,Speech and Signal Processing ,volume 3,pages 289–292,1992.2. C.M.Bishop,A.Blake,and B.Marthi.Super-resolution enhancement of video.InC.M.Bishop and B.Frey (Eds.),Proceedings Artificial Intelligence and Statistics ,2003.3. C.J.Harris and M.Stephens.A combined corner and edge detector.In Proceedings 4th Alvey Vision Conference ,pages 147–151,1988.4.M.G.Kang and S.Chaudhuri,editors.Super-resolution image reconstruction .IEEE Signal Processing Magazine,vol.20,no.3,2003.5.J.Mateos,M.Vega,R.Molina,and A.K.Katsaggelos.Bayesian image estimation from an incomplete set of blurred,undersampled low resolution images.In 1st Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA2003),LNCS 2652,pages 445–452,2003.516Salvador Villena et al.6.R.Molina,M.Vega,J.Abad,and A.K.Katsaggelos.Parameter estimation inbayesian high-resolution image reconstruction with multisensors.IEEE Trans.on Image Processing,12:1655–1667,2003.7. B.D.Ripley.Spatial Statistics.John Wiley,New York,1981.8.H.Stark and P.Oskoui.High-resolution image recovery from image-plane arrays,using convex projections.Journal of the Optical Society A,6(11):1715–1726,1989.9. B.C.Tom,N.P.Galatsanos,and A.K.Katsaggelos.Reconstruction of a highresolution image from multiple low resolution images.In S.Chaudhuri,editor, Super-Resolution Imaging,chapter4,pages73–105.Kluwer Academic Publishers, 2001.10. B.C.Tom and A.K.Katsaggelos.Reconstruction of a high-resolution imageby simultaneous registration,restoration,and interpolation of low-resolution im-ages”.In Proceedings of the IEEE International Conference on Image Processing, volume2,pages539–542,1995.11.M.Vega,J.Mateos,R.Molina,and A.K.Katsaggelos.Bayesian parameter esti-mation in image reconstruction from subsampled blurred observations.In IEEE International Conference on Image Processing(ICIP-2003),volume II,pages969–973,2003.12.N.A.Woods,N.P.Galatsanos,and A.K.Katsaggelos.Em-based simultaneousregistration,restoration,and interpolation of super resolved images.In Proceedings of the IEEE International Conference on Image Processing,volume II,pages303–306,2003.。

合集下载

无损检测介绍英文版

无损检测介绍英文版

The use of noninvasive techniques to determine the integrity of a material, component or structure
or quantitatively measure some characteristic of an object.
Eddy current testing is particularly well suited for detecting surface cracks but can also be used to make electrical conductivity and coating thickness measurements. Here a small surface probe is scanned over the part surface in an attempt to detect a crack.
Magnetic Particle Inspection
The part is magnetized. Finely milled iron particles coated with a dye pigment are then applied to the specimen. These particles are attracted to magnetic flux leakage fields and will cluster to form an indication directly over the discontinuity. This indication can be visually detected under proper lighting conditions.
图像的分割和配准中英文翻译

图像的分割和配准中英文翻译

外文文献资料翻译：李睿钦指导老师：刘文军Medical image registration with partial dataSenthil Periaswamy，Hany FaridThe goal of image registration is to find a transformation that aligns one image to another. Medical image registration has emerged from this broad area of research as a particularly active field. This activity is due in part to the many clinical applications including diagnosis, longitudinal studies, and surgical planning, and to the need for registration across different imaging modalities (e.g., MRI, CT, PET, X-ray, etc.). Medical image registration, however, still presents many challenges. Several notable difficulties are (1) the transformation between images can vary widely and be highly non-rigid in nature; (2) images acquired from different modalities may differ significantly in overall appearance and resolution; (3) there may not be a one-to-one correspondence between the images (missing/partial data); and (4) each imaging modality introduces its own unique challenges, making it difficult to develop a single generic registration algorithm.In estimating the transformation that aligns two images we must choose: (1) to estimate the transformation between a small number of extracted features, or between the complete unprocessed intensity images; (2) a model that describes the geometric transformation; (3) whether to and how to explicitly model intensity changes; (4) an error metric that incorporates the previous three choices; and (5) a minimization technique for minimizing the error metric, yielding the desired transformation.Feature-based approaches extract a (typically small) number of corresponding landmarks or features between the pair of images to be registered. The overall transformation is estimated from these features. Common features include corresponding points, edges, contours or surfaces. These features may be specified manually or extracted automatically. Fiducial markers may also be used as features;these markers are usually selected to be visible in different modalities. Feature-based approaches have the advantage of greatly reducing computational complexity. Depending on the feature extraction process, these approaches may also be more robust to intensity variations that arise during, for example, cross modality registration. Also, features may be chosen to help reduce sensor noise. These approaches can be, however, highly sensitive to the accuracy of the feature extraction. Intensity-based approaches, on the other hand, estimate the transformation between the entire intensity images. Such an approach is typically more computationally demanding, but avoids the difficulties of a feature extraction stage.Independent of the choice of a feature- or intensity-based technique, a model describing the geometric transform is required. A common and straightforward choice is a model that embodies a single global transformation. The problem of estimating a global translation and rotation parameter has been studied in detail, and a closed form solution was proposed by Schonemann. Other closed-form solutions include methods based on singular value decomposition (SVD), eigenvalue-eigenvector decomposition and unit quaternions. One idea for a global transformation model is to use polynomials. For example, a zeroth-order polynomial limits the transformation to simple translations, a first-order polynomial allows for an affine transformation, and, of course, higher-order polynomials can be employed yielding progressively more flexible transformations. For example, the registration package Automated Image Registration (AIR) can employ (as an option) a fifth-order polynomial consisting of 168 parameters (for 3-D registration). The global approach has the advantage that the model consists of a relatively small number of parameters to be estimated, and the global nature of the model ensures a consistent transformation across the entire image. The disadvantage of this approach is that estimation of higher-order polynomials can lead to an unstable transformation, especially near the image boundaries. In addition, a relatively small and local perturbation can cause disproportionate and unpredictable changes in the overall transformation. An alternative to these global approaches are techniques that model the global transformation as a piecewise collection of local transformations. For example, the transformation between each local region may bemodeled with a low-order polynomial, and global consistency is enforced via some form of a smoothness constraint. The advantage of such an approach is that it is capable of modeling highly nonlinear transformations without the numerical instability of high-order global models. The disadvantage is one of computational inefficiency due to the significantly larger number of model parameters that need to be estimated, and the need to guarantee global consistency. Low-order polynomials are, of course, only one of many possible local models that may be employed. Other local models include B-splines, thin-plate splines, and a multitude of related techniques. The package Statistical Parametric Mapping (SPM) uses the low-frequency discrete cosine basis functions, where a bending-energy function is used to ensure global consistency. Physics-based techniques that compute a local geometric transform include those based on the Navier–Stokes equilibrium equations for linear elastici and those based on viscous fluid approaches.Under certain conditions a purely geometric transformation is sufficient to model the transformation between a pair of images. Under many real-world conditions, however, the images undergo changes in both geometry and intensity (e.g., brightness and contrast). Many registration techniques attempt to remove these intensity differences with a pre-processing stage, such as histogram matching or homomorphic filtering. The issues involved with modeling intensity differences are similar to those involved in choosing a geometric model. Because the simultaneous estimation of geometric and intensity changes can be difficult, few techniques build explicit models of intensity differences. A few notable exceptions include AIR, in which global intensity differences are modeled with a single multiplicative contrast term, and SPM in which local intensity differences are modeled with a basis function approach.Having decided upon a transformation model, the task of estimating the model parameters begins. As a first step, an error function in the model parameters must be chosen. This error function should embody some notion of what is meant for a pair of images to be registered. Perhaps the most common choice is a mean square error (MSE), defined as the mean of the square of the differences (in either feature distance or intensity) between the pair of images. This metric is easy to compute and oftenaffords simple minimization techniques. A variation of this metric is the unnormalized correlation coefficient applicable to intensity-based techniques. This error metric is defined as the sum of the point-wise products of the image intensities, and can be efficiently computed using Fourier techniques. A disadvantage of these error metrics is that images that would qualitatively be considered to be in good registration may still have large errors due to, for example, intensity variations, or slight misalignments. Another error metric (included in AIR) is the ratio of image uniformity (RIU) defined as the normalized standard deviation of the ratio of image intensities. Such a metric is invariant to overall intensity scale differences, but typically leads to nonlinear minimization schemes. Mutual information, entropy and the Pearson product moment cross correlation are just a few examples of other possible error functions. Such error metrics are often adopted to deal with the lack of an explicit model of intensity transformations .In the final step of registration, the chosen error function is minimized yielding the desired model parameters. In the most straightforward case, least-squares estimation is used when the error function is linear in the unknown model parameters. This closed-form solution is attractive as it avoids the pitfalls of iterative minimization schemes such as gradient-descent or simulated annealing. Such nonlinear minimization schemes are, however, necessary due to an often nonlinear error function. A reasonable compromise between these approaches is to begin with a linear error function, solve using least-squares, and use this solution as a starting point for a nonlinear minimization.译文：部分信息的医学图像配准Senthil Periaswamy，Hany Farid图像配准的目的是找到一种能把一副图像对准另外一副图像的变换算法。

超分辨率技术(英文)

超分辨率技术(英文)

Super Resolution ProjectDeveloper’s GuideKarim KrichaneOctober19,2006AbstractThis document aims at giving a clear explanation of the different key parts of this project,so that anyone who wants to contribute to it may get up to speed in a minimal time.Furthermore,we define in this paper standards that need to be followed in order to allow easy collaboration.1Contents1Introduction3 2Motion estimation4 3Image Reconstruction521IntroductionIn many applications today,image resolution has become very impor-tant.While being a strong marketing argument for entry-level digital cameras,a high resolution sensor can greatly improve image quality if combined with good optics on a high-end digital SLR camera.What’s more,higher resolution in surveillance cameras can make it possible to see much more details,which could for instance help an investigation. Unfortunately,increasing the resolution at the sensor level implies buy-ing new equipment,which might befinancially prohibitive.The goal of Super Resolution is to increase the resolution of an im-age by using many images very similar to it.Note that in this paper, a higher resolution image is not only an image that has more pixels, but is an image that has a greater resolution power(i.e.more details are visible).From these similar pictures of the same scene,where the camera has moved very slightly,we can gather all the information nec-essary that will in turn enable us to reconstruct a higher resolution image.The ideal scenario is if we can obtain four images,where the three last are shifted from thefirst one respectively by0.5pixel on the x-axis,by0.5pixel on the y-axis,and both0.5pixel on the x and y-axis.This makes it then possible to double the resolution in both dimensions without any kind of interpolation.Super Resolution consists of two main steps:image registration and image reconstruction.Thefirst estimates the motion between the dif-ferent pictures,while the latter uses this information in order to re-construct the high resolution image.32Motion estimationMotion estimation techniques should only return the translation and rotation information,in the following format:[delta est,phi est]=method(...)Where:All outputs must be formated exactly in this way in the implemen-tation itself of the MATLAB function.Note:The MATLAB function imrotate and circshift work with the same conventions,i.e.:4imrotate(im,25,’crop’)and circshift(im,[3,10])However,the custom shift function included in this project takes theparameters in the opposite way,i.e.:shift(im,10,3)in order to achieve the same result3Image ReconstructionReconstruction techniques should accept the input images as2D“grayscale”images1,packed in a cell data structure,given by:im={im1,im2,im3,im4,...}Images can then be extracted from the cell using the MATLAB com-mand:im{i},where i is the image number.1This means that an RGB image mustfirst be divided into three2D matrices and the function will reconstruct each“layer”individually.5。

车牌识别中图像超分辨复原方法的研+究

车牌识别中图像超分辨复原方法的研+究

河北工业大学硕士学位论文车牌识别中图像超分辨复原方法的研究摘要车辆牌照识别是车辆检测中的一个重要环节，它在交通管理中占有重要的地位，有着多种应用。

车辆牌照识别首先是要看清楚车牌图像，提高图像质量，再进行车牌识别，才会有很好的识别效果。

要看清楚车牌图像，有一种方法就是超分辨复原。

本文针对车牌图像退化现象，基于小波分析和超分辨分析等图像增强技术的图像处理方法，对于车牌图像超分辨复原技术开展了较为深入的研究，从低分辨率图像中复原出高分辨率图像，通过分析目前存在的理论与方法，就以下方面得到了新的改进算法：(1) 一般的车牌图像会存在图像退化的现象，经过预处理的图像可能仍然无法识别或大幅降低识别准确性，所以需要提高图像的分辨率以得到较高质量车牌图像。

提高图像的空间分辨率主要取决于超分辨技术，本文对各种超分辨方法进行了研究和比较，提出了基于小波域的超分辨复原改进算法。

(2) 超分辨方法可以得到较高分辨率的图像，对于车牌图像序列通过运动估计分析出图像间点的运动信息，再通过运动补偿得到一幅超分辨率车牌图像。

本文利用超分辨率和运动估计相结合的算法，该算法由对低分辨率图像的估计到高分辨率图像的运动补偿，得到了很好的结果。

关键字：车牌识别,超分辨,运动估计i车牌识别中图像超分辨复原方法的研究iiRESEARCH OF SUPER-RESOLUTION RESTORATION IN PLATE LICENSE RECOGNITIONABSTRACTPlate license recognition is an important part of vehicles detection and traffic managementwith a variety of applications. The first step before recognition is to see Plate license image clearly and to enhance image quality, the recognition will have a very good effect. To see clearly license plate images, there is a method that is super-resolution restoration.For image degradation, this paper based on wavelet analysis and super-resolution and other image processing technology, doing a lot of researches in super-resolution restoration in license plate recognition from a low-resolution image, through analysis of existing theories and methods, has received the following new improved algorithm:(1) The general license plate image exists image degradation phenomenon, after pretreatment image could still be unable to identify or substantially reduce the recognition accuracy, so it is necessary to improve the image resolution in order to improve the image quality. Improvements in image spatial resolution depends primarily on super-resolution techniques, the paper on the various super-resolution methods were studied and compared, Presents a method based on wavelet domain of super-resolution improved algorithm for image restoration.(2) Super-resolution methods can get high-resolution images, through analysis of points motion estimation between the license plate image sequence, and the motion compensation we also can get super-resolution plate images. This paper presents super-resolution and motion estimation algorithm, the algorithm leads to a good result, through the low-resolution images motion estimation and the high-resolution images motion compensation.KEY WORDS: Plate License Recognition, Super-resolution, Motion Estimation僦々Ｔ＊ｗＪＥｌｎ＾｝ｘｊＩ№ｍ月＃＊Ｊ日㈣！∞女％．月＃ｍＦ＊ｍ＾＃＃“‘㈣㈣ｚＰ｛呲ｉ¨ｑⅧ￥￥％干版丰：㈣删㈨㈣￥＂自ｒ№Ｉｒ｝Ⅲ＊＿；¨％ｑ¨“㈣舭千聪憬徽㈣栅帅＊＆女ＨⅡｎｍ￥■｛ｔｌｅｚ十㈣㈣ＭｍＪ№＊－㈨榭捕㈣㈣删１“＃目Ｌ＊☆撇∞Ⅱ０“｝１Ｕｒ№．ｍ圳ｗｍ¨㈣ｍＦ学橙可㈣Ⅲ目＊女∞∞ｎ＾±∞ｎ＃】＿ｉＪ＃ｔ】＾“‘㈣㈨蝴《ｎ日Ⅷ｛《＊＆＊）‘磊｝乎川川河北工业大学硕士学位论文第一章 绪论伴随着世界各国汽车数量的增加，城市交通状况日益受到人们的重视。