Paper data
Title:
Modelling and assessment of signaldependent noise for image denoising Author(s): Alparone Luciano, DET  Univ. Firenze Argenti Fabrizio, DET  Univ. Firenze Torricelli Gionatan, DET  Univ. Firenze Page numbers in the proceedings: Volume III pp 287290 Session: Image Representation and Transformation
Paper abstract
In this paper, a class of signaldependent noise models that are encountered in image processing applications is considered. They are defined by the gamma exponent, which rules the dependence on the signal of the noise, and by the variance of a stationary zeromean random process that generates the signaldependent noise. An observation noise term, zeromean, white and independent of the signal, is also considered to account for the electronics noise. A blind procedure is proposed for reliably measuring the model parameters directly from the noisy images irrespective of their texture content. Such methods are iteratively based on linear regression techniques applied to scatterplots of local firstorder statistics calculated on homogeneous areas and drawn with logarithmic scale. Adaptive LLMMSE filtering is embedded in the iteration stage to provide a rough estimate of noisefree image texture which allows to discriminate between homogeneous and textured pixels. Experiments on simulated noisy images demonstrate a high accuracy of noise assessment.
Paper
