Gaussian Noise

Gaussian noise, a ubiquitous source of error in data acquisition and processing, is a central focus in numerous scientific fields. Current research emphasizes mitigating its effects through advanced filtering techniques, including Gaussian and non-local means filters, and incorporating noise models into machine learning algorithms, such as diffusion models and stochastic gradient descent, to improve robustness and accuracy. This work is crucial for enhancing the reliability of diverse applications, from image processing and deep learning to causal inference and time series analysis, where noisy data is prevalent. The development of novel algorithms and architectures that explicitly account for Gaussian noise is driving significant advancements across these fields.