Camera Noise Extraction From a Raw Single Image by Segmentation, Multiple Approximations, and Weighted Averaging

Digital cameras are widely used in a variety of applications, including medicine, security, material characterization, and metrology. To reduce cost, increase dynamic range, and minimize noise, accurate knowledge of camera parameters, including noise characteristics, is required. The most promising camera noise estimation methods use a single image. However, most methods estimate the noise of the acquired image rather than the intrinsic sensor noise of the camera. In this paper, a method is proposed to estimate main noise components from a single image: light temporal, light spatial and dark noise The method is based on segmentation, multi-interval approximation, and weighted averaging. The method was experimentally validated on a dataset obtained from a camera whose noise parameters were independently measured. The noise estimation error was reduced to single-digit or several tens of percent, which is more than 5 times better than using patch-based approximation. The proposed method can be used in signal and image processing, for camera selection, noise reduction, image authenticity, and so on.