Noise Generation

Noise generation research focuses on creating realistic noise models for various applications, ranging from improving the privacy of data streams to enhancing the realism of synthetic images and audio. Current efforts involve developing sophisticated algorithms, including diffusion models and generative adversarial networks (GANs), to synthesize noise with accurate statistical properties and spatial correlations, often tailored to specific sensor types or imaging conditions. These advancements are crucial for improving data privacy techniques, training robust machine learning models with limited real-world data, and developing more accurate simulations for diverse fields like non-destructive testing and digital pathology.