Improved image fusion method based on curvelet transform
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摘要:考虑将曲波变换引入图像融合能够更好地提取原始图像,对一种新的图像融合方法—曲波变换图像融合法进行了研究。将图像序列进行曲波变换后,通过对所有图像的高频进行逆变换及域值处理来获得区域图。根据区域图中高频区域的边界点在每张图层上的活跃度不同求得区域边界的图层分布,利用插值获得高频区域的区域分布图。通过高频区域的膨胀求得整幅图的区域分布图,然后在曲波变换的变换域,利用区域分布图对多尺度的高频系数采用高斯加权求和;对低频系数采用取平均值的规则完成图像的融合。进行了图像融合实验,实验结果表明,与传统的小波变换及基于像素的曲波变换相比,提出的方法获得的融合图像边缘更清晰,更接近参考图像。Abstract:To extract the original features of a image exactly, a new image fusion method based on the curvelet transform is proposed. A series of original images are decomposed by the curvelet transform, and then a region image is obtained by an inverse curvelettransform and a domain processing. For the edges of high frequency areas, the edge distribution image is obtained by the activity of every area in different images and the region image is gotten by an interpolation. Finally, the image fusion is accomplished by Gaussian distribution sums for high frequency coefficients and by mean values for low frequency coefficients in the transform domain. A image fusion experiment is undertaken, and the experiment results indicate that the proposed method can obtain a better fusion image with high contrast, clear edges and more closed to a reference image as compared with that of conventional wavelet transform and pixelbased curvelet transform methods.
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