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提出了一种基于BayesShrink小波阈值去噪算法和稀疏字典学习算法(K-SVD)相结合的图像去噪算法.针对现有的小波去噪算法只处理了细节子带系数,而没有处理近似子带的系数最终导致去噪效果带有局限性的问题,在实际应用中,噪声不仅改变了细节子带系数同时还改变了近似子带的系数,提出了使用K-SVD算法处理图像小波变换近似子带系数以改进现有小波阈值图像去噪算法的效果的缺陷,仿真实验结果表明:改进后的算法能够有效的去除图像的高斯噪声,提高图像的峰值信噪比,明显的改善图像的视觉效果. 相似文献
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一种基于非采样Contourlet变换红外图像与可见光图像融合算法 总被引:5,自引:1,他引:5
针对同一场景红外图像与可见光图像的融合问题,提出了一种基于非采样Contourlet变换(Nonsubsampled Contourlet Transform,NSCT)图像融合算法.算法首先采用NSCT对源图像进行多尺度、多方向分解,得到低频子带系数和各带通方向子带系数.然后,针对低频子带系数的选择,提出了一种基于红外图像与可见光图像物理特征的"加权平均"系数选择方案;针对各带通方向子带系数的选择,结合人眼视觉特性,提出了一种基于区域能量匹配的系数选择方案,得到融合图像的NSCT系数.最后经过NSCT逆变换得到融合图像.实验结果表明该算法可获得较理想的融合图像,其融合效果优于传统的基于离散小波变换以及离散小波框架变换的图像融合算法. 相似文献
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基于3维SPIHT编码的超光谱图像压缩 总被引:3,自引:0,他引:3
提出一种针对超光谱图像压缩的3维SPIHT编码算法.通过对超光谱图像进行3维小波变换,同时去除像素数据间的空间冗余和谱间冗余.针对变换后得到的小波系数,构造一种3维空间方向树结构,并用经3维扩展后的SPIHT算法(3D SPIHT算法)对小波系数进行量化编码.实验证明,基于3维小波变换的3维SPIHT编码算法在对超光谱图像压缩时,表现出了优良的率失真性能.并且算法复杂度适中,具有嵌入式特性. 相似文献
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针对传统的图像对比度增强方法所存在的一些问题,提出了一种基于形态小波变换的图像增强算法.首先,将低对比度图像采用基于提升格式的形态Haar小波进行小波变换,得到小波系数,该小波变换实现结构简单,计算量低,并且有利于图像重要的几何信息在低分辨率空间中的保留.然后,对低频子带小波系数,采用直方图双向均衡方法进行调整,对高频子带小波系数进行基于软阈值滤波的细节系数增强,将调整后的小波系数反变换到空域上,得到增强后的结果.最后,给出几种增强算法实验结果的比较和分析,表明该算法对低对比度图片的增强是非常有效的,可以有效地增强图像的细节信息,保持图像的边缘特征,改善图像的视觉效果. 相似文献
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基于TLS的正交小波变换红外图像去噪 总被引:3,自引:0,他引:3
提出了一种基于总体最小二乘的正交小波变换红外图像去噪算法。对红外图像进行离散正交小波变换,分别对各个分解层的高频子带,通过总体最小二乘算法估计小波系数,获得各个高频子带信号的估计系数,然后通过正交小波反变换得到去噪图像。仿真结果表明,该红外图像去噪算法能有效去除加性红外图像噪声,在信噪比、直方图匹配等方面都有较大改善,并获得了良好的主观视觉效果。 相似文献
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提出了一种基于尺度间和尺度内相关性的平稳小波变换红外图像去噪方法.首先对红外图像进行离散平稳小波变换,分别对各个分解层的高频子带,利用不同尺度小波系数形成的系数向量,通过线性最小均方误差估计小波系数,获得各个高频子带的估计系数,再利用小波系数尺度内的邻域相关性对小波系数进行修正,然后通过小波反变换得到去噪图像.仿真结果表明,考虑尺度间和尺度内相关性的平稳小波红外图像去噪算法能有效地去除红外图像噪声,在信噪比和视觉质量上要优于单纯考虑尺度间相关性的去噪方法. 相似文献
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一种应用于指纹识别系统的指纹图像压缩算法 总被引:1,自引:1,他引:0
针对应用于指纹识别系统中指纹图像的压缩编码问题,提出了一种改进的基于四叉树分类的网格编码量化(QTCQ)的指纹图像压缩算法.该算法对小波变换后的高频系数采用2×2的DCT变换进一步集中能量,并对变换后的系数进行系数重排以使得高频子带内的重要系数集中于相应子带的低频位置,再通过基于四叉树的网格编码量化进行量化编码.仿真结果表明,该算法比WSQ和JPEG2000等均具有更好的压缩性能. 相似文献
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A new framework for complex wavelet transforms 总被引:9,自引:0,他引:9
Fernandes F.C.A. van Spaendonck R.L.C. Burrus C.S. 《Signal Processing, IEEE Transactions on》2003,51(7):1825-1837
Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce two-stage mapping-based complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, non-redundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and non-redundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we can exploit this flexibility to create the complex double-density DWT (CDDWT): a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3/sup m/-1/2/sup m/-1) in m dimensions. To the best of our knowledge, no other transform achieves all these properties at a lower redundancy. 相似文献
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Multidimensional, mapping-based complex wavelet transforms. 总被引:4,自引:0,他引:4
Felix C A Fernandes Rutger L C van Spaendonck C Sidney Burrus 《IEEE transactions on image processing》2005,14(1):110-124
Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce multidimensional, mapping-based, complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, nonredundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and nonredundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we exploit this flexibility to create the complex double-density DWT: a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3M - 1)/(2M - 1) in M dimensions. No other transform achieves all these properties at a lower redundancy, to the best of our knowledge. By exploiting the advantages of our multidimensional, mapping-based complex wavelet transforms in seismic signal-processing applications, we have demonstrated state-of-the-art results. 相似文献
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小波变换的图像编码方法,不仅拥有传统编码的优点,能够消除图像中的统计冗余,并且,其多分辨率的特性提供了消除非统计冗余信息的良好机制。基于离散小波变换(DWT)理论,介绍了DWT在数字图像压缩中的应用,使用零树编码实现了数字图像压缩,并同时保持原图像在各种分辨率下的精细结构,该方法对消除图像中非统计冗余信息提供了有效途径。 相似文献
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针对采用下采样滤波器结构的轮廓波、轮廓小波在图像去噪过程中会引入伪吉布斯现象,利用小波变换(WT)和非下采样方向滤波器组(NDFB)构造了一种新的多尺度、多分辨率图像的非下采样轮廓小波变换(NWCT)。WT去除了拉普拉斯金字塔滤波器(LPF)的计算冗余,NDFB保证了该变换具有平移不变性。为了验证该变换的有效性,对其进行了图像去噪实验。实验结果表明,所提出方法能获得比WT、轮廓波变换(CT)、轮廓小波变换(WCT)更高的峰值信噪比(PSNR),并且能够很好地抑制伪吉布斯现象。 相似文献
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Chao-Chun Liu Dao-Qing Dai 《IEEE transactions on image processing》2009,18(11):2593-2599
We propose a novel facial representation based on the dual-tree complex wavelet transform for face recognition. It is effective and efficient to represent the geometrical structures in facial image with low redundancy. Moreover, we experimentally verify that the proposed method is more powerful to extract facial features robust against the variations of shift and illumination than the discrete wavelet transform and Gabor wavelet transform. 相似文献
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Ali Zifan Mohammad Hassan Moradi Shahriar Gharibzadeh 《Signal, Image and Video Processing》2010,4(2):177-185
In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. We
use the denoising capabilities of decimated and undecimated multiwavelet transforms, DMWT and UMWT respectively, for the removal
of noise from microarray data. Multiwavelet transforms, with appropriate initialization, provide sparser representation of
signals than wavelet transforms so that their difference from noise can be clearly identified. Also, the redundancy of the
UMWT transform is particularly useful in image denoising in order to capture the salient features such as noise or transients.
We compare this method with the discrete and stationary wavelet transforms, denoted by DWT and SWT, respectively, and the
Wiener filter for denoising microarray images. Results show enhanced image quality using the proposed approach, especially
in the undecimated case in which the results are comparable and often outperform that of the stationary wavelet transform.
Both multiwavelet transforms outperform the DWT and the Wiener filter. 相似文献
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为了去除高光谱影像的数据冗余,提高高光谱影像处理的精度和效率,提出了一种基于波段指数的高光谱影像波段选择算法。采用小波变换对高光谱图像数据进行去噪处理,依据联合偏度-峰度指数将波段进行分组,再根据波段指数的大小确定相对较小指数的波段,并将其作为冗余波段进行去除,从而得到最小波段集。结果表明,利用该波段集和全波段所选的端元是一致的,在不影响端元提取的前提下,最大程度地去除了冗余波段,而且该波段集与全波段的分类精度较接近。该算法在波段选择过程中具有可行性与有效性,为降低高光谱影像维数提供了一种帮助。 相似文献