Multiresolution directional-oriented image transform based on Gaussian derivatives
Multiresolution directional-oriented image transform based on Gaussian derivatives
复制标题
基于高斯导数的多分辨率定向图像变换
DOI：
10.1117/12.449717
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
J. L. Silván
中科院分区：
文献类型：
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作者：
B. Escalante;J. L. Silván
本文以尺度空间理论为基础，推导了一种多通道图像表示模型。该模型的灵感来源于生物学的洞察力，它包含了人类视觉的一些重要特性，例如Young提出的早期视觉的高斯导数模型。我们提出的图像变换在多个尺度上使用与Hermite变换类似的分析算子，但我们方法的综合方案综合了不同尺度上所有通道的响应。该方案的优点是：1)分析和综合算子都是高斯导数。这允许实现过程中的简单性。2)算子函数具有更好的空频局部化特性，根据Wilson在人类视觉通道上的结果，可以将相邻的尺度分开一个八度。3)对于二维信号，很容易在不同尺度上分析局部方位。从高斯导数和离散二项式滤波之间的渐近关系也导出了离散近似。在这项工作中，我们展示了如何将所提出的变换应用于图像编码问题。实际考虑也令人担忧。
In this work, a multi-channel model for image representation is derived based on the scale-space theory. This model is inspired in biological insights and it includes some important properties of human vision such as the Gaussian derivative model for early vision proposed by Young. The image transform that we propose in this work uses similar analysis operators as the Hermite transform at multiple scales, but the synthesis scheme of our approach integrates the responses of all channels at different scales. The advantages of this scheme are: 1) both analysis and synthesis operators are Gaussian derivatives. This allows for simplicity during implementation. 2) The operator functions possess better space-frequency localization, and it is possible to separate adjacent scales one octave apart, according to Wilson's results on human vision channels. 3) In the case of 2-D signals, it is easy to analyze local orientations at different scales. A discrete approximation is also derived from an asymptotic relation between the Gaussian derivatives and the discrete binomial filters. We show in this work how the proposed transform can be applied to the problem of image coding. Practical considerations are also of concern.