IPOL Journal · Image Processing On Line
- 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.
Download
- full text manuscript: PDF low-res. (652.7K) PDF (10.1M) [?]
- source code: TAR/GZ
History
- Note from the editor: the original source code was modified on 2021-08-22 to add a missing return *this in the implementation of operator += in Vector.h. The original version of the code is available here.
- Note from the editor: the manuscript of the article was modified on 2022-01-01 to include information about its editors. The original version of the manuscript is available here.
Acknowledgments
- This work was partially supported by BPIFrance and Région Ile de France in the FUI 18 Plein Phare project.
