Analysis and Extension of the PCA Method, Estimating a Noise Curve from a Single Image
Miguel Colom, Antoni Buades
→ BibTeX
@article{ipol.2016.124,
    title   = {{Analysis and Extension of the PCA Method, Estimating a Noise Curve from a Single Image}},
    author  = {Colom, Miguel and Buades, Antoni},
    journal = {{Image Processing On Line}},
    volume  = {6},
    pages   = {365--390},
    year    = {2016},
    doi     = {10.5201/ipol.2016.124},
}
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.2016.124}}
published
2016-12-29
reference
Miguel Colom, and Antoni Buades, Analysis and Extension of the PCA Method, Estimating a Noise Curve from a Single Image, Image Processing On Line, 6 (2016), pp. 365–390. https://doi.org/10.5201/ipol.2016.124

Communicated by Jean-Michel Morel, Yohann Tendero
Demo edited by Miguel Colom

Abstract

In the article 'Image Noise Level Estimation by Principal Component Analysis', S. Pyatykh, J. Hesser, and L. Zheng propose a new method to estimate the variance of the noise in an image from the eigenvalues of the covariance matrix of the overlapping blocks of the noisy image. Instead of using all the patches of the noisy image, the authors propose an iterative strategy to adaptively choose the optimal set containing the patches with lowest variance. Although the method measures uniform Gaussian noise, it can be easily adapted to deal with signal-dependent noise, which is realistic with the Poisson noise model obtained by a CMOS or CCD device in a digital camera.

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