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首页> 外文会议>Conference on Image Processing: Algorithms and Systems III; 20040119-20040121; San Jose,CA; US >An Advanced Image Fusion Algorithm Based on Wavelet Transform - Incorporation with PCA and Morphological Processing
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An Advanced Image Fusion Algorithm Based on Wavelet Transform - Incorporation with PCA and Morphological Processing

机译：基于小波变换-结合PCA和形态学处理的先进图像融合算法。

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摘要

There are numerous applications for image fusion, some of which include medical imaging, remote sensing, nighttime operations and multi-spectral imaging. In general, the discrete wavelet transform (DWT) and various pyramids (such as Laplacian, ratio, contrast, gradient and morphological pyramids) are the most common and effective methods. For quantitative evaluation of the quality of fused imagery, the root mean square error (RMSE) is the most suitable measure of quality if there is a "ground truth" image available; otherwise, the entropy, spatial frequency or image quality index of the input images and the fused images can be calculated and compared. Here, after analyzing the pyramids' performance with the four measures mentioned, an advanced wavelet transform (aDWT) method that incorporates principal component analysis (PCA) and morphological processing into a regular DWT fusion algorithm is presented. Specifically, at each scale of the wavelet transformed images, a principle vector was derived from two input images and then applied to two of the images' approximation coefficients (i.e., they were fused by using the principal eigenvector). For the detail coefficients (i.e., three quarters of the coefficients), the larger absolute values were chosen and subjected to a neighborhood morphological processing procedure which served to verify the selected pixels by using a "filling" and "cleaning" operation (this operation filled or removed isolated pixels in a 3-by-3 local region). The fusion performance of the advanced DWT (aDWT) method proposed here was compared with six other common methods, and, based on the four quantitative measures, was found to perform the best when tested on the four input image types. Since the different image sources used here varied with respect to intensity, contrast, noise, and intrinsic characteristics, the aDWT is a promising image fusion procedure for inhomogeneous imagery.
机译：图像融合有许多应用，其中一些包括医学成像，遥感，夜间操作和多光谱成像。通常，离散小波变换（DWT）和各种金字塔（例如拉普拉斯算子，比率，对比度，梯度和形态金字塔）是最常见和有效的方法。对于融合图像质量的定量评估，如果有“地面真实”图像可用，则均方根误差（RMSE）是最合适的质量度量；否则，可以计算和比较输入图像和融合图像的熵，空间频率或图像质量指标。在此，在通过上述四种措施分析金字塔的性能之后，提出了一种将主成分分析（PCA）和形态处理结合到常规DWT融合算法中的高级小波变换（aDWT）方法。具体地，在小波变换图像的每个尺度上，从两个输入图像导出主矢量，然后将其应用于图像的两个近似系数（即，通过使用主特征矢量将它们融合）。对于细节系数（即系数的四分之三），选择较大的绝对值，然后对其进行邻域形态处理，该过程用于通过使用“填充”和“清理”操作（选中的该操作填充或移除3 x 3局部区域中的孤立像素）。将本文提出的高级DWT（aDWT）方法的融合性能与其他六种常见方法进行了比较，并且基于四种定量方法，发现在四种输入图像类型上进行测试时，其融合性能最佳。由于此处使用的不同图像源在强度，对比度，噪声和固有特性方面均存在差异，因此aDWT是用于非均匀图像的有前途的图像融合程序。

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