Title :
A Closed-Form Approximation of the Exact Unbiased Inverse of the Anscombe Variance-Stabilizing Transformation
Author :
Mäkitalo, Markku ; Foi, Alessandro
Author_Institution :
Dept. of Signal Process., Tampere Univ. of Technol., Tampere, Finland
Abstract :
We presented an exact unbiased inverse of the Anscombe variance-stabilizing transformation in [M. Mäkitalo and A. Foi, “Optimal inversion of the Anscombe transformation in low-count Poisson image denoising,” IEEE Trans. Image Process., vol. 20, no. 1, pp. 99-109, Jan. 2011.] and showed that when applied to Poisson image denoising, the combination of variance stabilization and state-of-the-art Gaussian denoising algorithms is competitive with some of the best Poisson denoising algorithms. We also provided a MATLAB implementation of our method, where the exact unbiased inverse transformation appears in nonanalytical form. Here, we propose a closed-form approximation of the exact unbiased inverse in order to facilitate the use of this inverse. The proposed approximation produces results equivalent to those obtained with the accurate (nonanalytical) exact unbiased inverse, and thus, notably better than one would get with the asymptotically unbiased inverse transformation that is commonly used in applications.
Keywords :
Gaussian noise; approximation theory; image denoising; Anscombe variance stabilizing transformation; Gaussian denoising; Poisson denoising algorithms; closed form approximation; exact unbiased inverse; image denoising; Approximation algorithms; Approximation methods; Image denoising; Imaging; Integrated circuits; Noise; Noise reduction; Denoising; Poisson noise; photon-limited imaging; variance stabilization;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2011.2121085