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所在平台: Udemy |
课程主页: https://www.udemy.com/course/fpga-filter/
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**课程名称：**FPGA 滤波器 **课程概述：** 本课程系列专注于FPGA上的滤波器算法设计与仿真。目前包含三个主要部分： 1. **FPGA均值（平均）滤波器：** * 均值滤波器是一种线性的、窗口式的平滑滤波器，常用于对信号（如图像）进行平滑处理。 * 其基本原理是，将信号（图像）中的每个元素替换为其邻域内元素的平均值。 * 均值滤波器具有低通滤波器的特性。 2. **FPGA中值滤波器：** * 中值滤波器是一种非线性的数字滤波技术，常用于去除图像或信号中的噪声。 * 它是一种重要的预处理步骤，可以提高后续处理（如边缘检测）的效果。 * 中值滤波器在数字图像处理中应用广泛，因为它在特定条件下能够有效去除噪声同时保留边缘。 * 其核心思想是逐个遍历信号，并将每个元素替换为其邻域内元素的中值。邻域的模式被称为“窗口”，这个窗口会逐个元素地在整个信号上滑动。 3. **FPGA高斯滤波器：** * 高斯滤波器是一种在电子学和信号处理中使用的滤波器，其脉冲响应为一个高斯函数（或一个近似函数）。 * 高斯滤波器的主要优点在于对阶跃函数输入没有超调，并能最小化上升和下降时间。 * 这种特性与其在时域上具有最小群延迟的特点密切相关，使其被认为是理想的时域滤波器。 * 数学上，高斯滤波器通过与一个高斯函数进行卷积来处理输入信号，这一过程也称为维尔斯特拉斯变换。 * 高斯滤波器在示波器和数字通信系统等领域具有重要应用。
This series of lessons will focus on the FPGA filter algorithm design and simulation. It currently includes three parts (It might include more in the future):(1) FPGA Mean (Average) Filter:The Mean filter, or average filter is windowed filter of linear class, that smoothes signal (image). The filter works as low-pass one. The basic idea behind filter is for any element of the signal (image) take an average across its neighborhood.(2) FPGA Median Filter:The median filter is a non-linear digital filtering technique, often used to remove noise from an image or signal. Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image). Median filtering is very widely used in digital image processing because, under certain conditions, it preserves edges while removing noise, also having applications in signal processing.The main idea of the median filter is to run through the signal entry by entry, replacing each entry with the median of neighboring entries. The pattern of neighbors is called the "window", which slides, entry by entry, over the entire signal.(3) FPGA Gaussian Filter:In electronics and signal processing, a Gaussian filter is a filter whose impulse response is a Gaussian function (or an approximation to it, since a true Gaussian response is physically unrealizable as it has infinite support). Gaussian filters have the properties of having no overshoot to a step function input while minimizing the rise and fall time. This behavior is closely connected to the fact that the Gaussian filter has the minimum possible group delay. It is considered the ideal time domain filter, just as the sinc is the ideal frequency domain filter. These properties are important in areas such as oscilloscopes and digital telecommunication systems.Mathematically, a Gaussian filter modifies the input signal by convolution with a Gaussian function; this transformation is also known as the Weierstrass transform.
