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Image fusion using daubechies complex wavelet transform and lifting wavelet transform: A multiresolution approach

机译:使用Daubechies复数小波变换和提升小波变换的图像融合:多分辨率方法

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For the retrieval of complementary information from medical images fusion of multimodal medical images is necessary. The fused image should not introduce any undesired feature and the fusion process should possess all relevant information. In more precise diagnosis and better treatment the medical image fusion facilities are used. Higher accuracy and reliability are provided by fused image. Shift sensitivity, poor directionality and lack of any phase information are real valued wavelet transform based fusion method properties. Therefore Daubechies complex wavelet transform is used for image fusion, since it has less computational requirements and availability of phase information. Here proposed a new multilevel Daubechies complex wavelet transform (DCxWT) based multimodal medical image fusion method which follows multi-resolution principle. Fusion of complex wavelet coefficients of source images using maximum selection rule takes place in this method. The proposed fusion method visually and quantitatively compared with LWT using entropy and standard deviation metrics. Robustness of the proposed method tested against Gaussian, salt and pepper and speckle noise. Comparison results clearly show that the proposed fusion scheme with DCxWT outperforms existing LWT based fusion method.
机译:为了从医学图像中检索补充信息,需要融合多模式医学图像。融合图像不应引入任何不希望的特征,并且融合过程应拥有所有相关信息。在更精确的诊断和更好的治疗中,使用了医学图像融合设备。融合图像提供了更高的准确性和可靠性。位移灵敏度,差的方向性和缺少任何相位信息都是基于实值小波变换的融合方法属性。因此,Daubechies复数小波变换用于图像融合,因为它具有较少的计算需求和相位信息的可用性。在此基础上,提出了一种遵循多分辨率原理的新的基于多级道伯奇斯复数小波变换(DCxWT)的多峰医学图像融合方法。该方法利用最大选择规则对源图像的复数小波系数进行融合。所提出的融合方法在视觉上和定量上与使用熵和标准偏差度量的LWT进行了比较。所提出的方法的鲁棒性针对高斯,盐和胡椒以及斑点噪声进行了测试。比较结果清楚地表明,提出的DCxWT融合方案优于现有的基于LWT的融合方法。

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