Noise Decomposition

Noise decomposition involves separating signals from noise components, aiming to improve signal processing and generation across various domains. Current research focuses on developing novel decomposition methods, often employing diffusion models, Gaussian mixture models, or sine-transient-noise decompositions, to enhance the quality and control of signal reconstruction and synthesis. These techniques find applications in diverse fields, including image and video generation, reinforcement learning, and audio processing, where effective noise handling is crucial for improved performance and realism. The resulting advancements contribute to more robust and efficient algorithms for handling noisy data in numerous applications.