Noise Prior

Noise priors are statistical representations of noise characteristics used to improve the accuracy and robustness of various machine learning models, particularly in image and video processing, 3D reconstruction, and signal enhancement. Current research focuses on developing methods to effectively estimate and incorporate these priors, often within neural network architectures like transformers and neural fields, leading to advancements in conditional denoising, 3D scene reconstruction with improved geometric accuracy, and high-fidelity video synthesis. The effective use of noise priors is crucial for improving the performance of these models in real-world scenarios where noisy data is prevalent, impacting applications ranging from augmented and virtual reality to medical imaging and speech processing.