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Estimating the amount of defocus through a wavelet transform approach

机译:通过小波变换方法估计散焦量

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This paper presents a new technique for defocus estimation of a captured image. In our method, a ratio of the wavelet coefficients of high frequency correspond to a same image point at two different levels is used. For an edge point, it is shown that the ratio is related to the amount of defocus. Let α be the ratio of wavelet coefficients at the first level to that at the second level. The value of α decreases as the amount of defocus increase. In our experiments of iris image analysis, when ? is larger than 0.5 the number of feature points in an image almost remain constant. It means that the image is little defocused and available for image recognition. Compared with Fourier methods, this technique is more robust. In addition, this method is fast enough to be used in auto-focus system for tracking moving objects.
机译:本文提出了一种新技术,用于捕获图像的散焦估计。在我们的方法中,使用了高频小波系数的比率,该比率对应于两个不同级别的同一图像点。对于边缘点,示出了该比率与散焦量有关。设α为第一级的小波系数与第二级的小波系数之比。 α的值随着散焦量的增加而减小。在我们的虹膜图像分析实验中,何时?大于0.5时,图像中的特征点数几乎保持不变。这意味着图像几乎没有散焦,可用于图像识别。与傅立叶方法相比,该技术更加健壮。另外,该方法足够快以用于自动聚焦系统中以跟踪运动对象。

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