Wavelet Based

Wavelet-based methods leverage the multi-resolution analysis capabilities of wavelets to extract features and solve problems across diverse scientific domains. Current research focuses on integrating wavelets with deep learning architectures like neural networks (including Physics-Informed Neural Networks and Transformers), and applying these hybrid models to tasks such as image processing (denoising, inpainting, classification), time series analysis (forecasting, anomaly detection), and signal processing (speech enhancement, threat detection). This approach offers advantages in efficiency, accuracy, and interpretability compared to traditional methods, impacting fields ranging from medical imaging and materials science to environmental monitoring and network security.