title: Practical signal-dependent noise parameter estimation from a single noisy image.
abstract: The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.
authors.forename: Xinhao
authors.initials: X
authors.lastname: Liu
authors.forename: Masayuki
authors.initials: M
authors.lastname: Tanaka
authors.forename: Masatoshi
authors.initials: M
authors.lastname: Okutomi