A Minimum Entropy Image Denoising Algorithm - Minimizing Conditional Entropy in a New Adaptive Weighted K-th Nearest Neighbor Framework for Image Denoising

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. The focus is set on denoising applications. Natural image statistics are consistent with a Markov random field (MRF) model for the image structure. Thus in a restoration process attention must be paid to the spatial correlation between adjacent pixels.The proposed approach minimizes the conditional entropy of a pixel knowing its neighborhood. The estimation procedure of statistical properties of the image is carried out in a new adaptive weighted k-th nearest neighbor (AWkNN) framework. Experimental results show the interest of such an approach. Restoration quality is evaluated by means of the RMSE measure and the 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 Minimum Entropy Image Denoising Algorithm - Minimizing Conditional Entropy in a New Adaptive Weighted K-th Nearest Neighbor Framework for Image Denoising. International Conference on Computer Vision Theory and Applications, Jan 2008, Funchal, Madeira, Portugal. ⟨hal-00379326⟩