VMD_2D_python

Python3 implementation of 2D Variational Mode Decomposition using NumPy

Overview

Written by: Dodge(Lang HE) asdsay@gmail.com
Updated date: 2024-12-13

VMD (Variational Mode Decomposition) is a signal processing tool that decomposes input signals into different band-limited IMFs (Intrinsic Mode Functions).

VMD_2D processes 2D signals (both dimensions should typically have the same length). This project VMD_2D_Python is an implementation based on the MATLAB version, providing spectrum-based decomposition of a 2D input signal into k band-separated modes.

Dependencies

• NumPy (required for core VMD2D function)
• OpenCV (optional, for image reading)
• Matplotlib (optional, for visualization)

Usage Notes

In this project, a grayscale image is used for testing.

Important Configuration

In VMD2D.py, lines 34-35:

# Maximum number of iterations
N = 3000

While the original MATLAB code used a fixed value of 3000, our testing showed that the sample image does not converge at this value. However, setting N = 100 provides practically equivalent results for this particular image. Feel free to adjust the N value based on your needs.

References

For detailed information about Variational Mode Decomposition, please refer to the original paper:

K. Dragomiretskiy, D. Zosso, "Variational Mode Decomposition", IEEE Trans. on Signal Processing
DOI: http://dx.doi.org/10.1109/TSP.2013.2288675

二维VMD（变分模态分解）的Python3实现，使用NumPy

概述

作者：Dodge asdsay@gmail.com 更新日期：2023-11-16

VMD（变分模态分解）是一种信号处理算法，可以将输入信号分解为不同带限的内禀模态函数（IMFs）。 VMD_2D意味着我们正在处理二维信号（通常两个维度应该长度相同）。项目是MATLAB中实现的模仿。基于频谱的二维输入信号分解为k个带分离模式。 本项目VMD_2D_Python是参考于其在MATLAB中的实现。基于频谱的二维输入信号分解为k个带分离模式。

依赖项

• NumPy（核心VMD2D函数所需）
• OpenCV（可选，用于图像读取）
• Matplotlib（可选，用于可视化）

使用说明

在这个项目中，我用一张灰度图片进行测试。本项目VMD_2D_Python仅需要Numpy，但我们还需要OpenCV和matplotlib两个库来读取和显示图片。

重要配置

在 VMD2D.py第34-35行：

# Maximum number of iterations
N = 3000

在原始的Matlab代码中，N是固定值3000。然而在测试中，样本图片计算的误差没有收敛。发现对于这张图片，如果设置N = 100，实际效果几乎就收敛了。请用户更改N值测试效果。

参考文献

如果需要描述变分模态分解的文档，可参阅原始论文： K. Dragomiretskiy, D. Zosso, "Variational Mode Decomposition", IEEE Trans. on Signal Processing
DOI: http://dx.doi.org/10.1109/TSP.2013.2288675