February 2024
Intermediate to advanced
387 pages
7h 20m
Chinese
Content preview from Python图像处理实战Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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第2章 采样、傅里叶变换与卷积
本章将阐述如何实现二维信号在时域和频域之间的相互转换。首先，我们将提出“采样”这样一个用于调整图像大小的重要概念，列举采样中所遇到的挑战，并试着用Python库中的函数来解决这些问题。其次，介绍图像的量化。量化是指在图像中每个像素所使用的比特数，以及它对图像质量的影响。本章还将介绍离散傅里叶变换（Discrete Fourier Transform，DFT）。学完本章，读者可以用numpy和scipy函数库中的快速傅里叶变换（Fast Fourier Transform，FFT）算法实现DFT，并将其应用于图像的实现。
本章还将介绍二维卷积的相关内容，它能显著加快卷积的速度。首先，我们将给出卷积定理的基本概念，并通过一个实例来澄清 “相关”和“卷积”这两个概念之间由来已久的混淆。其次，通过一个SciPy的示例，展示如何通过应用互关联的模板来查找图像中特定模式的位置。
本章还将介绍一些滤波技术，以及如何用Python库实现它们。读者应该会对用这些滤波器进行图像去噪后所得到的结果感兴趣。
本章主要包括以下内容：
- 图像形成——采样和量化；
- 离散傅里叶变换；
- 理解卷积。
2.1 图像形成——采样和量化
本节将描述图像形成的两个重要概念——采样和量化，并介绍如何使用PIL和scikit-image库通过采样和颜色量化来调整图像的大小。我们将使用一种实战的方法，并在实践中定义这两个概念。
从导入如下所需要的包开始，如下面的代码所示：
% matplotlib inline # for inline image display inside notebook from PIL import Image from skimage.io import imread, ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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