Multi Scale Wavelet

Multi-scale wavelet analysis is emerging as a powerful tool for improving the interpretability and robustness of machine learning models, particularly in image and signal processing. Current research focuses on applying wavelet transforms to enhance explainable AI (XAI) methods, enabling the identification of not only *where* important features are located but also *what* those features represent in terms of underlying structure. This approach is proving valuable in diverse applications, including remote sensing, medical image segmentation, and 3D shape generation, by improving model reliability and facilitating more trustworthy decision-making. The resulting advancements contribute to increased confidence in AI systems across various scientific and engineering domains.