Noise Modeling

Noise modeling in various domains, from image processing to quantum computing, aims to accurately characterize and simulate noise to improve system performance and robustness. Current research focuses on developing sophisticated noise models, often incorporating physical priors or learned representations using neural networks (e.g., normalizing flows, convolutional neural networks), and integrating these models into algorithms for noise reduction, data augmentation, and improved model training. These advancements have significant implications for diverse applications, including enhancing image quality, improving the reliability of quantum devices, and boosting the accuracy of deep learning models trained on noisy data.