基于支持向量回归的图像超分辨率重建算法

文章提出一种新的基于支持向量回归(SVR)和稀疏表示的图像超分辨重建算法。SVR对输入数据有良好预测输出类别能力。图像统计表明,图像块可以从过完备字典中通过稀疏线性组合很好的表示。对一幅低分辨率输入图像,可以将图像超分辨问题视为在高分辨图像中估计其像素位置。与传统的支持向量回归方法相比,本文采用的特征是不同类型的图像块的稀疏表示。研究表明,稀疏表示作为特征对噪声有一定的鲁棒性。实验结果表明,本文方法与传统支持向量回归方法相比在图像重建质量上有一定的优势。     

Optimal spatial adaptation for patch-based image denoising.

A novel adaptive and patch-based approach is proposed for image denoising and representation. The method is based on a pointwise selection of small image patches of fixed size in the variable neighborhood of each pixel. Our contribution is to associate with each pixel the weighted sum of data points within an adaptive neighborhood, in a manner that it balances the accuracy of approximation and the stochastic error, at each spatial position. This method is general and can be applied under the assumption that there exists repetitive patterns in a local neighborhood of a point. By introducing spatial adaptivity, we extend the work earlier described by Buades et al. which can be considered as an extension of bilateral filtering to image patches. Finally, we propose a nearly parameter-free algorithm for image denoising. The method is applied to both artificially corrupted (white Gaussian noise) and real images and the performance is very close to, and in some cases even surpasses, that of the already published denoising methods.     

基于特征元素和关联规则的图象分类方法

图象分类是搜索引擎中的重要模块.本文提出了一种基于特征元素的图象分类方法.特征元素与特征向量相比能够根据人的主观感知来提取图象的视觉特征.与传统的基于特征向量的图象分类方法不同,本文提出的图象分类方法不计算特征空间中特征向量之间的距离,而是通过关联规则挖掘发现图象的特征元素与图象所属类别之间的联系.本文实现了该分类算法并将其与一种基于特征向量的图象分类方法NFL相比较.实验的结果证实了所提方法的优越性.     

基于特征元素的图像检索方法

该文提出了一种基于特征元素的新的图像俭索算法。持征元素与特征向量相比更注重根据人的主观感知来表达图像的视觉特征.在特征元素的基础上,该文先定义了图像间的相似性度量,即特征元素间的距离,又分别实现了对不同特征元素类别的距离计算。检索实验表明,基于特征元素的图像检索算法能够取得更符合人们视觉感知的结果。     

Recursive binary dilation and erosion using digital linestructuring elements in arbitrary orientations

Performing morphological operations such as dilation and erosion of binary images, using very long line structuring elements is computationally expensive when performed brute-force following definitions. We present two-pass algorithms that run at constant time for obtaining binary dilations and erosions with all possible length line structuring elements, simultaneously. The algorithms run at constant time for any orientation of the line structuring element. Another contribution of this paper is the use of the concept of orientation error between a continuous line and its discrete counterpart. The orientation error is used in determining the minimum length of the basic digital line structuring element used in obtaining what we call dilation and erosion transforms. The transforms are then thresholded by the length of the desired structuring element to obtain the dilation and erosion results. The algorithms require only one maximum operation for erosion transform and only one minimum operation for dilation transform, and one thresholding step and one translation step per result pixel. We tested the algorithms on Sun Sparc Station 10, on a set of 240x250 salt and pepper noise images with probability of a pixel being a 1-pixel set to 0.25, for orientations of the normals of the structuring elements in the range [pi/2,3pi/2] and lengths, in pixels, in the range [5,145]. We achieved a speed up of about 50 (and for special orientations theta in {(pi/2), (3pi/4), pi, (5pi/4), (3pi/2)} a speed up of about 100) when the structuring elements had lengths of 145 pixels, over the brute-force methods in these experiments. We compared the results of our dilation algorithm with those of the algorithm discussed by Soille et al. (see IEEE Trans. Pattern Anal. Machine Intell., vol.18, p.562-67, 1996) and showed that for binary dilation (and erosion since it is just the dilation of the background with the reflected structuring element) our algorithm performed better and achieved a speed up of about four when dilation or erosion transform alone is obtained.     

基于多特征扩展pLSA模型的场景图像分类

场景图像分类近年来受到人们的广泛关注,而基于统计模型的方法更是场景分类中的研究热点。我们提出了一种新的基于多特征融合和扩展pLSA模型的场景图像分类框架。对每幅图像首先用多尺度规则分割确定局部基元,然后提取每个局部基元的多分辨率直方图矩特征和SIFT特征,最后用扩展的概率生成模型对图像集进行建模,测试。我们的方法不仅能够很好的表示图像的语义特性而且在模型的训练阶段是无监督的。我们针对目前常用的3个数据库,做了三组对比实验,均取得了比以前的方法更好的识别结果。      

Elastic registration for retinal images based on reconstructed vascular trees

The vascular tree of the retina is likely the most representative and stable feature for eye fundus images in registration. Based on the reconstructed vascular tree, we propose an elastic matching algorithm to register pairs of fundus images. The identified vessels are thinned and approximated using short line segments of equal length that results a set of elements. The set of elements corresponding to one vascular tree are elastically deformed to optimally match the set of elements of another vascular tree, with the guide of an energy function to finally establish pixel relationship between both vascular trees. The mapped positions of pixels in the transformed retinal image are computed to be the sum of their original locations and corresponding displacement vectors. For the purpose of performance comparison, a weak affine model based fast chamfer matching technique is proposed and implemented. Experiment results validated the effectiveness of the elastic matching algorithm and its advantage over the weak affine model for registration of retinal fundus images.     

A Gaussian Mixture Model-based regularization method in adaptive image restoration

A GMM （Gaussian Mixture Model） based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth, edge or detail texture region according to variance-sum criteria function of the feature vectors. Then parameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network （Hopfield-NN） is used to optimize the objective function of image restoration, and network weight value matrix is updated by the output of GMM. Since GMM is used, the regularization parameters share properties of different kind of regions. In addition, the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system, and it has strong generalization capability. Comparing with non-adaptive and some adaptive image restoration algorithms, experimental results show that the proposed algorithm obtains more preferable restored images.     

Automatic detection of red lesions in digital color fundus photographs

The robust detection of red lesions in digital color fundus photographs is a critical step in the development of automated screening systems for diabetic retinopathy. In this paper, a novel red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame et al. (1998) with two important new contributions. The first contribution is a new red lesion candidate detection system based on pixel classification. Using this technique, vasculature and red lesions are separated from the background of the image. After removal of the connected vasculature the remaining objects are considered possible red lesions. Second, an extensive number of new features are added to those proposed by Spencer-Frame. The detected candidate objects are classified using all features and a k-nearest neighbor classifier. An extensive evaluation was performed on a test set composed of images representative of those normally found in a screening set. When determining whether an image contains red lesions the system achieves a sensitivity of 100% at a specificity of 87%. The method is compared with several different automatic systems and is shown to outperform them all. Performance is close to that of a human expert examining the images for the presence of red lesions.     

病变视网膜图像血管网络的自动分割

现有的视网膜血管分割方法大多只针对正常的视网膜图像进行分割,不能实现对发生病变的视网膜图像的分割.为此,提出了一种新的病变视网膜图像血管网络分割方法.该方法首先采用向量场散度方法获得病变视网膜图像中大部分血管的中心线,然后计算出中心线上各像素点的方向信息并采用改进的定向局部对比度方法检测出中心线两侧的血管像素,最后对获得的血管段末端进行反向外推追踪,分割出最终的血管网络.通过对通用的STARE眼底图像库中所有病变视网膜图像的实验仿真,结果表明本文算法获得了0.9426的ROC曲线面积和0.9502的准确率,算法性能明显优于Hoover算法和Benson等提出的算法.此外,本文算法还克服了Benson算法的局限性,对不同类型的病变视网膜图像都具有较好的鲁棒性.     

基于重组DCT系数子带能量直方图的图像检索

现在许多图像采用JPEG格式存储,检索这些图像通常要先解压缩,然后提取基于像素域的特征矢量进行图像检索。己有文献提出直接在DCT域进行图像检索的方法,这样可以降低检索的时间复杂度。本文提出对JPEG图像的DCT系数利用多分辨率小波变换的形式进行重组,对整个数据库中所有图像的DCT系数重组得到的若干子带,分别建立子带能量直方图,而后采用Morton顺序建立每幅图像的索引,并采用变形B树结构组织图像数据库用于图像检索。     

基于非负矩阵分解的SAR图像目标识别

 特征提取是合成孔径雷达自动目标识别的关键技术,同时也是难点问题之一。本文提出了一种基于非负矩阵分解算法与Fisher线性判别方法的合成孔径雷达图像目标识别的方法,通过基于基向量非负加权组合的形式构建SAR目标图像,能充分利用目标的局部空间结构信息提取目标特征信息实现目标识别。首先将水平集分割预处理后的SAR目标图像样本构成初始矩阵,然后利用非负矩阵分解后得到的权向量作为目标图像的特征向量,再通过依据Fisher线性判别构成的分类器,实现对MSTAR数据中3类目标的识别,并与目前已有的几种典型方案进行对比。试验结果表明该方法是可行且有效的,并能够明显提高对目标识别的稳定性和正确率。     

Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction

Retinal images can be used in several applications, such as ocular fundus operations as well as human recognition. Also, they play important roles in detection of some diseases in early stages, such as diabetes, which can be performed by comparison of the states of retinal blood vessels. Intrinsic characteristics of retinal images make the blood vessel detection process difficult. Here, we proposed a new algorithm to detect the retinal blood vessels effectively. Due to the high ability of the curvelet transform in representing the edges, modification of curvelet transform coefficients to enhance the retinal image edges better prepares the image for the segmentation part. The directionality feature of the multistructure elements method makes it an effective tool in edge detection. Hence, morphology operators using multistructure elements are applied to the enhanced image in order to find the retinal image ridges. Afterward, morphological operators by reconstruction eliminate the ridges not belonging to the vessel tree while trying to preserve the thin vessels unchanged. In order to increase the efficiency of the morphological operators by reconstruction, they were applied using multistructure elements. A simple thresholding method along with connected components analysis (CCA) indicates the remained ridges belonging to vessels. In order to utilize CCA more efficiently, we locally applied the CCA and length filtering instead of considering the whole image. Experimental results on a known database, DRIVE, and achieving to more than 94% accuracy in about 50 s for blood vessel detection, proved that the blood vessels can be effectively detected by applying our method on the retinal images.     

Multiscale estimation of multiple orientations based on morphological directional openings

This paper introduces a novel approach to estimate multiple orientations at each pixel of a gray image at different scales. The main orientations are provided by a bank of directional openings. Gathering the responses of the filtered directional openings provide at each pixel a discrete sequence which is the directional signature. Then, the directional signature is interpolated by cubic B-splines, and the multiple orientations at each pixel are obtained by means of peak detection in the continuous directional signature. This procedure is performed using structuring elements with different lengths which results in a multiscale approach. The comparison with other existing methods as well as the experimental results on images shows the ability of the proposed method to detect multiple orientations in textured images at different scales with high accuracy.     

基于光电图像特征的自主测速方法研究

为了提高无人机低空飞行时自主测速能力，本文提出了一种基于光电图像特征的无人机自主测速方法，首先利用光电设备获取存在相同地面景象的连续两帧光电图像，并对前后帧图像分别进行点特征、线特征检测，对提取的图像特征进行判断，提取有效的图像特征；然后对提取的图像特征进行特征关联，根据关联的图像特征像素位置计算图像特征的像素位移矢量，剔除偏差较大的位移矢量；最后根据光电设备参数及位移矢量特性计算无人机对地速度。利用无人机搭载光电设备进行了飞行试验，试验结果表明本文方法减少了计算量，具有较好实用性，能够满足无人机等低空飞行平台自主测速要求。     

Classification of remotely sensed images using decimal coded morphological profiles

In this paper, we propose a novel method for pixel classification of remotely sensed images. The proposed method exploits the spatial information of image pixels using morphological profiles produced by structuring elements of different sizes and shapes. Morphological profiles produced by multiple structuring elements are combined into a single feature by decimal coding. The advantage of proposed feature is that it can effectively utilize the potential of multiple morphological profiles without increasing the complexity of feature space. The proposed approach was tested on remotely sensed images with known ground truths, and performance was improved up to 27 % in the overall accuracy results over existing techniques.     

Computer Aided Segmentation of Blockages in Coronary Heart Images Using Canfis Classifier

Coronary vessel blockage segmentation is the fundamental component which extracts significant features from angiogram images to detect heart disease. This paper proposes an automated method of blockage segmentation from coronary angiogram images using coactive adaptive neuro fuzzy inference system (CANFIS) classifier. The proposed method consists of preprocessing, feature extraction and classification. The vessels in the coronary image are enhanced using preprocessing technique and then features are extracted from these images which are given to the CANFIS classifier. This classifier classifies the given test coronary image into either normal or abnormal. Further, the blockage is detected and segmented if the proposed system classifies the test image as abnormal. The proposed method achieves 99.76% sensitivity, 99.9% specificity and 99.9% accuracy for blockage vessel pixel detection.     

Retinal blood vessel segmentation using line operators and support vector classification

In the framework of computer-aided diagnosis of eye diseases, retinal vessel segmentation based on line operators is proposed. A line detector, previously used in mammography, is applied to the green channel of the retinal image. It is based on the evaluation of the average grey level along lines of fixed length passing through the target pixel at different orientations. Two segmentation methods are considered. The first uses the basic line detector whose response is thresholded to obtain unsupervised pixel classification. As a further development, we employ two orthogonal line detectors along with the grey level of the target pixel to construct a feature vector for supervised classification using a support vector machine. The effectiveness of both methods is demonstrated through receiver operating characteristic analysis on two publicly available databases of color fundus images.