Signal Denoising

Signal denoising aims to remove unwanted noise from signals while preserving important features, crucial for accurate analysis and interpretation across diverse fields. Current research emphasizes developing robust and efficient denoising methods using a variety of approaches, including deep learning architectures (like convolutional and recurrent neural networks, and hypergraph neural networks), signal processing techniques (such as wavelet transforms and anisotropic diffusion filtering), and hybrid models combining both. These advancements improve the quality of data in applications ranging from biomedical signal processing (EEG, ECG) and structural health monitoring to audio and optical fiber sensing, ultimately enhancing the reliability and accuracy of scientific findings and technological applications.