Five Good Reasons for Complex-Valued Transforms in Image Processing
Five Good Reasons for Complex-Valued Transforms in Image Processing
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DOI：
10.1007/978-3-319-08801-3_15
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发表时间：
2014
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影响因子：
--
通讯作者：
B. Forster
中科院分区：
文献类型：
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作者：
B. Forster
1946年，Dennis Gabor为一维信号引入了解析信号。这种函数的复杂性使我们能够获得它们的幅度和相位信息，并从此对信号随时间变化的特性有了很好的理解。然而，除了傅里叶变换和Gabor变换外，复值变换在图像处理中仍然没有找到自己的位置，这两种变换在许多情况下都证明了它们的性能。在本章中，我们给出了考虑更一般的复变换用于图像分析的五个理由。我们将讨论这些转变的挑战和优势。
In 1946, Dennis Gabor introduced the analytic signal for one-dimensional signals. This complexification of functions gives access to their amplitude and phase information and has since then given well-interpretable insight into the properties of the signals over time. However, complex-valued transforms still have not found their place in image processing, except for the Fourier transform and the Gabor transform, which both have proven their performance in many contexts. In this chapter, we give five reasons to consider more general complex transforms for image analysis. We discuss the challenges and advantages of those transforms.