Pub. online:8 Dec 2020Type:Research ArticleOpen Access
Journal:Informatica
Volume 32, Issue 2 (2021), pp. 357–370
Abstract
It is a challenging task to prevent the staircase effect and simultaneously preserve sharp edges in image inpainting. For this purpose, we present a novel nonconvex extension model that closely incorporates the advantages of total generalized variation and edge-enhancing nonconvex penalties. This improvement contributes to achieve the more natural restoration that exhibits smooth transitions without penalizing fine details. To efficiently seek the optimal solution of the resulting variational model, we develop a fast primal-dual method by combining the iteratively reweighted algorithm. Several experimental results, with respect to visual effects and restoration accuracy, show the excellent image inpainting performance of our proposed strategy over the existing powerful competitors.
Journal:Informatica
Volume 20, Issue 1 (2009), pp. 151–163
Abstract
To restore the underexposure image, an illumination compensation inpainting model which employs the joint-diffused partial differential equations (PDEs) is proposed. Firstly, the novel model compensates the illumination effect in multi-scaled underexposure images respectively. Secondly, the information in the fused compensated image is restored by PDEs which diffuse the geometric property and gray information into the target region simultaneously. Experimental results demonstrate that the novel model can properly restore scratches while compensating the illumination effect in underexposure image, and the joint-diffused PDEs which are employed in it lead to a better performance than the conventional PDE inpainting models.