A nonparametric minimum entropy image deblurring algorithm

Cesario Vincenzo Angelino (1) , Eric Debreuve (1) , Michel Barlaud (1)

1 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe IMAGES-CREATIVE

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.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Cesario Vincenzo Angelino, Eric Debreuve, Michel Barlaud. A nonparametric minimum entropy image deblurring algorithm. IEEE International Conference on Acoustics, Speech, and Signal Processing, Mar 2008, Las Vegas, Nevada, United States. ⟨hal-00379328⟩