sRGB Noise

sRGB noise, the noise present in standard RGB images after camera processing, poses a significant challenge in image processing due to its complex, non-linear distribution stemming from sensor noise and the camera's image signal processor (ISP). Current research focuses on accurately modeling this noise using data-driven approaches, particularly employing generative adversarial networks (GANs) and normalizing flows, often in hybrid architectures to leverage the strengths of each. Accurate sRGB noise modeling is crucial for developing effective denoising algorithms and creating realistic training datasets for low-level vision tasks, ultimately improving image quality in consumer applications and scientific image analysis.