共查询到20条相似文献,搜索用时 15 毫秒
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Raouf Hamzaoui Dietmar Saupe Michael Hiller 《Journal of Visual Communication and Image Representation》2001,12(4):450
Optimal fractal image coding is an NP-hard combinatorial optimization problem, which consists of finding in a finite set of contractive affine mappings one whose unique fixed point is closest to the original image. Current fractal image schemes are based on a greedy suboptimal algorithm known as collage coding. In a previous paper, Hamzaoui, Hartenstein, and Saupe proposed a local search algorithm that iteratively improves an initial solution found by collage coding. For a standard fractal scheme based on quadtree image partitions, peak-signal-to-noise ratio (PSNR) gains are up to 0.8 dB. However, the algorithm is time-consuming because it involves many iteration steps, each of which requires the computation of the fixed point of an affine mapping. In this paper, we provide techniques that drastically reduce the complexity of the algorithm. Moreover, we show that the algorithm is also successful with a state-of-the-art fractal scheme based on highly adaptive image partitions. 相似文献
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针对红外目标类型识别问题,提出了一种基于灰关联分析的红外目标识别算法.该算法在对红外图像进行边缘检测后,提取目标的奇异值特征作为关联参数,通过比较特征参数的灰关联值实现目标识别.该算法根据目标各个特征指标稳定性的不同,采用了一种灰关联加权系数的方法,仿真实验验证了该方法在红外目标识别问题上的有效性,并对目标旋转和平移有一定的适应能力. 相似文献
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对于图像放大技术而言,重要的就是要权衡到图像质量以及计算复杂度.传统的基于线性或三次样条插值的方法会带来图像模糊和锯齿边缘等失真,为了解决这一问题,人们提出一种基于迭代和学习的算法,但是这种方法带来了很高的计算复杂度.综合以上几点本文提出了一种基于自适应协方差的图像放大方法(adaptive covariance-based edge diffusion,ACED).该方法能很好地权衡图像放大性能和复杂度之间的关系.在这种方法中,提出了一种联合边缘判别准则,并自适应选择扩散模板来估计局部协方差系数,以高效的减少图像放大带来的失真.实验结果表明,所提出的方法在主观质量和客观质量上都有很大的提升,同时也具有较低的计算复杂度. 相似文献
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为了解决日益严重的盗版问题,提出了一种基于小波包变换的自适应水印算法。对宿主图像进行小波包分解,对水印图像进行小波分解。根据视觉感知特性将水印的低频分量自适应地嵌入到宿主图像的低频分量中,嵌入强度根据图像的内容自适应地决定。水印的中高频分量用位置自适应的方法嵌入到图像的中高频分量中。嵌入过程中充分利用Hilbert扫描曲线的良好特性。实验结果表明,该算法对常见的图像处理操作具有较强的鲁棒性。 相似文献
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为了改善医学图像的视觉效果,提高图像的清晰度,使之更适合于机器的分析处理以及人的视觉特性,并突出病灶点,为病理学诊断和临床诊断提供可靠依据。设计了一个对医学图像十分具有针对性的图像增强系统。针对CT图像的电子噪声提出了基于修正维纳滤波的小波包去噪算法;针对B型超声图像的散斑噪声提出了基于脉冲耦合神经网络(PCNN)模型的小波自适应斑点噪声滤除算法;针对医学图像对比度低,边缘信息模糊等特点,提出了基于小波变换的医学图像增强算法。当噪声方差为0.01时,基于脉冲耦合神经网络(PCNN)模型的小波自适应斑点噪声滤除算法获得的PSNR比经Wiener滤波方法获得的PSNR高出9 dB。系统能快速找到噪声点进行定点去噪,能有效提高医学图像的对比度,增强边缘细节信息,突出病灶点的位置,从而达到较好的处理效果,为医疗工作者观察病症提供更加清晰准确的依据。 相似文献
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为了解决图像超分辨率重建中稀疏系数解的不精确问题,提出了一种自适应正则化级联稀疏矩阵的超分辨率重建算法。根据图像自身的特性,采用自适应正则化项对图像局部进行处理,实现图像的局部约束,构建基于自适应正则化的稀疏矩阵函数。另外,为了提高图像的可清晰性,采用基于全局约束的退化模型改进处理结构。测试结果表明,与其他常用算法相比,提出的自适应正则化的图像超分辨率重建算法能够构建更清晰的超分辨率图像。 相似文献
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Existing methods for image contrast enhancement focus mainly on the properties of the image to be processed while excluding any consideration of the observer characteristics. In several applications, particularly in the medical imaging area, effective contrast enhancement for diagnostic purposes can be achieved by including certain basic human visual properties. Here the authors present a novel adaptive algorithm that tailors the required amount of contrast enhancement based on the local contrast of the image and the observer's Just-Noticeable-Difference (JND). This algorithm always produces adequate contrast in the output image, and results in almost no ringing artifacts even around sharp transition regions, which is often seen in images processed by conventional contrast enhancement techniques. By separating smooth and detail areas of an image and considering the dependence of noise visibility on the spatial activity of the image, the algorithm treats them differently and thus avoids excessive enhancement of noise, which is another common problem for many existing contrast enhancement techniques. The present JND-Guided Adaptive Contrast Enhancement (JGACE) technique is very general and can be applied to a variety of images. In particular, it offers considerable benefits in digital radiography applications where the objective is to increase the diagnostic utility of images. A detailed performance evaluation together with a comparison with the existing techniques is given to demonstrate the strong features of JGACE. 相似文献
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在经典的双边全变差( BTV)超分辨率重建中,加权系数和正则化参数的恒定性导致重建结果边缘保持能力受限。为此,提出了一种自适应约束的BTV正则化先验模型。算法首先定义了图像的局部邻域残差均值以区分当前像素属于平坦区域还是边缘区域;然后针对加权系数的不变性导致边缘削弱的问题,利用边缘方向和垂直边缘方向扩散性的不同,设计自适应权重矩阵;最后根据代价函数的极值问题推导出迭代公式,从而进行图像的超分辨率重建,重建过程中采用自适应的方法确定正则化参数,以便求得代价函数的全局最优解,提高了算法的鲁棒性。实验结果表明:与双三次线性插值法和经典BTV算法相比,该算法取得了更好的视觉效果和更高的峰值信噪比,更多地保留了图像的边缘细节信息。 相似文献
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In this paper, we present an approach for medical ultrasound (US) image enhancement. It is based on a novel perceptual saliency measure which favors smooth, long curves with constant curvature. The perceptual salient boundaries of tissues in US images are enhanced by computing the saliency of directional vectors in the image space, via a local searching algorithm. Our measure is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. To restrain speckle noise during the enhancement process, an adaptive speckle suspension term is also combined into the proposed saliency measure. The results obtained on both simulated images and medical US data reveal superior performance of the novel approach over a number of commonly used speckle filters. Applications of US image segmentation show that although the proposed algorithm cannot remove the speckle noise completely and may discard weak anatomical structures in some case, it still provides a considerable gain to US image processing for computer-aided diagnosis. 相似文献
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算法的基本思路是: 首先寻找合适医学图像的自适应字典并利用稀疏矩阵理论进行图像降噪: 其次根据边缘的特点, 将降噪后的医学图像利用改进的Canny算子对图像进行边缘检测并利用差值把边缘点连接起来; 再次使用区域的连通性将相关区域合并起来; 最后我们将感兴趣区域提取出来. 本文通过介绍基于自适应学习的超完备字典训练算法, 并以此展开研究探索, 深入研究了基于KSVD算法的图像降噪方法, 通过MATLAB平台仿真, 验证了此方法的有效性. 相似文献
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This paper presents a fully automated segmentation method for medical images. The goal is to localize and parameterize a variety of types of structure in these images for subsequent quantitative analysis. We propose a new hybrid strategy that combines a general elastic template matching approach and an evolutionary heuristic. The evolutionary algorithm uses prior statistical information about the shape of the target structure to control the behavior of a number of deformable templates. Each template, modeled in the form of a B-spline, is warped in a potential field which is itself dynamically adapted. Such a hybrid scheme proves to be promising: by maintaining a population of templates, we cover a large domain of the solution space under the global guidance of the evolutionary heuristic, and thoroughly explore interesting areas. We address key issues of automated image segmentation systems. The potential fields are initially designed based on the spatial features of the edges in the input image, and are subjected to spatially adaptive diffusion to guarantee the deformation of the template. This also improves its global consistency and convergence speed. The deformation algorithm can modify the internal structure of the templates to allow a better match. We investigate in detail the preprocessing phase that the images undergo before they can be used more effectively in the iterative elastic matching procedure: a texture classifier, trained via linear discriminant analysis of a learning set, is used to enhance the contrast of the target structure with respect to surrounding tissues. We show how these techniques interact within a statistically driven evolutionary scheme to achieve a better tradeoff between template flexibility and sensitivity to noise and outliers. We focus on understanding the features of template matching that are most beneficial in terms of the achieved match. Examples from simulated and real image data are discussed, with considerations of algorithmic efficiency. 相似文献