Generalized signal-dependent noise model and parameter estimation for natural images

• A novel noise model of digital image pixel׳s is proposed.
• The heteroscedastic model for RAW image is extended to rendered images.
• This model is shown to outperform the state-of-the-art models.
• A simple yet efficient method for parameters estimation is also proposed.
• An application for denoising of digital still images in also presented.

The goal of this paper is to propose a generalized signal-dependent noise model that is more appropriate to describe a natural image acquired by a digital camera than the conventional Additive White Gaussian Noise model widely used in image processing. This non-linear noise model takes into account effects in the image acquisition pipeline of a digital camera. In this paper, an algorithm for estimation of noise model parameters from a single image is designed. Then the proposed noise model is applied with the Local Linear Minimum Mean Square Error filter to design an efficient image denoising method.