Simultaneous deconvolution and denoising using a second order variational approach applied to image super resolution

Amine Laghrib (1) , Mahmoud Ezzaki (2) , Mohammed El Rhabi (2) , Abdelilah Hakim (1) , Pascal Monasse (3) , Said Raghay (4)

1 UCA - Université Cadi Ayyad [Marrakech]
2 ENPC - École des Ponts ParisTech
3 LIGM - Laboratoire d'Informatique Gaspard-Monge
4 Faculté des Sciences Semlalia [Marrakech]

The aim of a Super Resolution (SR) technique is to construct a high-resolution image from a sequence of observed low-resolution ones of the same scene. One major roadblock of an SR reconstitution is removing noise and blur without destroying edges. We propose a novel multiframe image SR algorithm based on a convex combination of Bilateral Total Variation and a non-smooth second order variational regulariza-tion, using a controlled weighting parameter. We prove the existence of a minimizer of the proposed SR model in the space of functions of bounded Hessian, and we confirm the success of this approach in avoiding undesirable artifacts. The simulation results show the efficient performance of the proposed algorithm compared to other methods in the literature using two criteria, PSNR and SSIM.

Domains: Computer Vision and Pattern Recognition [cs.CV]

Amine Laghrib, Mahmoud Ezzaki, Mohammed El Rhabi, Abdelilah Hakim, Pascal Monasse, et al.. Simultaneous deconvolution and denoising using a second order variational approach applied to image super resolution. Computer Vision and Image Understanding, 2017, ⟨10.1016/j.cviu.2017.08.007⟩. ⟨hal-01616743⟩