Two-Dimensional Empirical Mode Decomposition

This text demonstrates the application of two-dimensional empirical mode decomposition (2D-EMD) for analyzing images and other two-dimensional signals. The decomposition method serves as an effective tool for algorithm verification and learning. From an implementation perspective, 2D-EMD typically involves iterative sifting processes to extract intrinsic mode functions (IMFs) through envelope estimation using interpolation techniques. We can further explore how to apply 2D-EMD to handle more complex datasets by implementing adaptive stopping criteria and boundary condition handling in the code. Additionally, we can investigate different decomposition techniques and algorithms, such as varying interpolation methods (e.g., cubic spline or radial basis functions) and extremum detection approaches, to improve the accuracy and effectiveness of decomposition results. Such implementations help us better understand and utilize two-dimensional signals, while establishing a solid foundation for further research and practical applications. The code structure typically includes functions for local extrema detection, envelope generation, and IMF separation, allowing researchers to modify parameters and observe their impact on decomposition quality.