Estimation du niveau de bruit par la détection non paramétrique de zones homogènes

Abstract : Most image processing applications rely on the knowledge of the noise level, yet there are relatively few methods that automatically estimate it in an image without strong assumptions. A major difficulty is that the noise level is usually signal dependent. We propose a two-step algorithm that estimates the noise level function, i.e., the noise variance as a function of the image intensity. Assuming only that the noise is spatially uncorrelated, homogeneous areas are detected based on Kendall's τ coefficient. The noise-level function is then assumed to be a second order polynomial and estimated by L 1 minimization from the statistics of these regions. Our numerical experiments show the efficiency of our estimator with a relative error under 10%, and the ability to use the noise estimation for image denoising for example.
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Camille Sutour, Charles-Alban Deledalle, Jean-François Aujol. Estimation du niveau de bruit par la détection non paramétrique de zones homogènes. Gretsi, Sep 2015, Lyon, France. ⟨hal-01241209⟩

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