IMAGE DENOISING IN MULTIPLICATIVE NOISE

Seelamantula, Chandra Sekhar and Blu, Thierry (2015) IMAGE DENOISING IN MULTIPLICATIVE NOISE. In: IEEE International Conference on Image Processing (ICIP), SEP 27-30, 2015, Quebec City, CANADA, pp. 1528-1532.

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Abstract

We 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.

Item Type:	Conference Proceedings
Series.:	IEEE International Conference on Image Processing ICIP
Publisher:	IEEE
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords:	multiplicative noise; unbiased risk estimation; Gamma distribution; speckle noise
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	17 May 2016 05:34
Last Modified:	17 May 2016 05:34
URI:	http://eprints.iisc.ac.in/id/eprint/53843

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