IEEE Trans Image Process - Image denoising with dominant sets by a coalitional game approach.

Tópicos

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Resumo

Dominant sets are a new graph partition method for pairwise data clustering proposed by Pavan and Pelillo. We address the problem of dominant sets with a coalitional game model, in which each data point is treated as a player and similar data points are encouraged to group together for cooperation. We propose betrayal and hermit rules to describe the cooperative behaviors among the players. After applying the betrayal and hermit rules, an optimal and stable graph partition emerges, and all the players in the partition will not change their groups. For computational feasibility, we design an approximate algorithm for finding a dominant set of mutually similar players and then apply the algorithm to an application such as image denoising. In image denoising, every pixel is treated as a player who seeks similar partners according to its patch appearance in its local neighborhood. By averaging the noisy effects with the similar pixels in the dominant sets, we improve nonlocal means image denoising to restore the intrinsic structure of the original images and achieve competitive denoising results with the state-of-the-art methods in visual and quantitative qualities.

Resumo Limpo

domin set new graph partit method pairwis data cluster propos pavan pelillo address problem domin set coalit game model data point treat player similar data point encourag group togeth cooper propos betray hermit rule describ cooper behavior among player appli betray hermit rule optim stabl graph partit emerg player partit will chang group comput feasibl design approxim algorithm find domin set mutual similar player appli algorithm applic imag denois imag denois everi pixel treat player seek similar partner accord patch appear local neighborhood averag noisi effect similar pixel domin set improv nonloc mean imag denois restor intrins structur origin imag achiev competit denois result stateoftheart method visual quantit qualiti

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