Histogram modeling --- Example

The gradient histogram and the Gaussian mixture approximation

Relevant data:

  • Image: lena.pgm

  • The standard deviation of Gaussian noise: 5

  • Number of terms in the mixture model: 3

  • Mixture model parameters: sigma={4.04,6.88,23.58}, weights={0.56,0.35,0.09}


Edge intensity histogram and Rayleigh mixture approximation

Relevant data:

  • Image: lena.pgm

  • The standard deviation of Gaussian noise: 5

  • Number of terms in the mixture model: 3

  • Mixture model parameters: sigma={3.96,6.78,21.53}, weights={0.54,0.36,0.10}

We tested dozens of images and they all got the similar results. This mixture density model on edge intensity is a novel approach we proposed for image analysis. Next, we are going to describe how to use this model to estimate noise and image quality.
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