Image denoising in multiplicative noise
Refereed conference paper presented and published in conference proceedings

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AbstractWe address the problem of denoising images corrupted by multiplicative noise. The noise is assumed to follow a Gamma distribution. Compared with additive noise distortion, the effect of multiplicative noise on the visual quality of images is quite severe. We consider the mean-square error (MSE) cost function and derive an expression for an unbiased estimate of the MSE. The resulting multiplicative noise unbiased risk estimator is referred to as MURE. The denoising operation is performed in the wavelet domain by considering the image-domain MURE. The parameters of the denoising function (typically, a shrinkage of wavelet coefficients) are optimized for by minimizing MURE. We show that MURE is accurate and close to the oracle MSE. This makes MURE-based image denoising reliable and on par with oracle-MSE-based estimates. Analogous to the other popular risk estimation approaches developed for additive, Poisson, and chi-squared noise degradations, the proposed approach does not assume any prior on the underlying noise-free image. We report denoising results for various noise levels and show that the quality of denoising obtained is on par with the oracle result and better than that obtained using some state-of-the-art denoisers.
All Author(s) ListSeelamantula C.S., Blu T.
Name of ConferenceIEEE International Conference on Image Processing, ICIP 2015
Start Date of Conference27/09/2015
End Date of Conference30/09/2015
Place of ConferenceQuebec City
Country/Region of ConferenceCanada
Detailed descriptionorganized by IEEE,
Volume Number2015-December
Pages1528 - 1532
LanguagesEnglish-United Kingdom
KeywordsGamma distribution, multiplicative noise, speckle noise, unbiased risk estimation

Last updated on 2021-18-01 at 00:32