Anisotropic Diffusion

Anisotropic diffusion is a signal processing technique that selectively smooths data while preserving important features like edges, unlike isotropic diffusion which treats all regions uniformly. Current research focuses on integrating anisotropic diffusion into various machine learning models, particularly diffusion probabilistic models (DPMs), to improve efficiency, accuracy, and robustness in tasks such as image generation, speech enhancement, and molecular conformation prediction. These advancements are impacting diverse fields, enabling improved performance in areas like medical image analysis, drug discovery, and remote sensing through more accurate and efficient data processing.