Wavelet Coefficient

Wavelet coefficients represent a signal's information across multiple scales and frequencies, offering a powerful tool for analyzing complex data. Current research focuses on leveraging wavelet coefficients within various machine learning architectures, such as physics-informed neural networks and generative adversarial networks, to improve efficiency and accuracy in tasks ranging from image registration and denoising to time series synthesis and image compression. This approach enhances the ability to capture localized features and handle high-frequency information, leading to improvements in model performance and interpretability across diverse scientific and engineering domains. The resulting advancements are impacting fields like medical imaging, infrastructure inspection, and signal processing.