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ICASSP
2008
IEEE

A nonparametric minimum entropy image deblurring algorithm

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A nonparametric minimum entropy image deblurring algorithm
In this paper we address the image restoration problem in the variational framework. Classical approaches minimize the Lp norm of the residual and rely on parametric assumptions on the noise statistical model. We relax this parametric hypothesis and we formulate the problem on the basis of nonparametric density estimates. The proposed approach minimizes the residual differential entropy. Experimental results with non gaussian distributions show the interest of such a nonparametric approach. Images quality is evaluated by means of the PSNR measure and SSIM index, more adapted to the human visual system.
Cesario Vincenzo Angelino, Eric Debreuve, Michel B
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICASSP
Authors Cesario Vincenzo Angelino, Eric Debreuve, Michel Barlaud
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