Quantile analysis of image sensor noise distribution

This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. Quantile analysis in pixel, wavelet, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor noise behavior. Noise model mismatch would likely result in image denoising that undersmoothes real sensor data.