Simplified noise model parameter estimation for signal-dependent noise

• We introduced a simplified signal-dependent noise model to easily estimate noise parameters.
• We showed that the robust median estimator is almost equivalent to the mean of the signal-dependent noise standard deviation.
• The linear equation of the noise standard deviation for an image pixel was constructed.
• The noise parameters were estimated by a simple linear regression method, and these are converted to the Poisson–Gaussian noise model.
• Compared to conventional methods, our method gave reasonable outputs.

In this paper, we present a noise parameter estimation method using a simplified signal-dependent noise model. The generic Poisson–Gaussian noise model is simplified to a Gaussian–Gaussian noise model. From the simplified noise model, we experimentally verify that the value obtained by the robust median estimator is almost the same as the mean of the noise standard deviation. Based on this property, the noise model parameters are estimated by the least square method. Simulation results show that the estimation performance using our proposed algorithm is compatible with the performance of the existing method. Our method can generate good parameter estimation results with reduced computational complexity.