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  4. A game theory approach for joint blind deconvolution and inpainting

A game theory approach for joint blind deconvolution and inpainting

MMC.
2023;
Volume 10, Number 3
: pp. 674–681
https://doi.org/10.23939/mmc2023.03.674
Received: February 15, 2023
Revised: July 08, 2023
Accepted: July 09, 2023

Mathematical Modeling and Computing, Vol. 10, No. 3, pp. 674–681 (2023)

Authors:
  1. N. Nasr
  2. N. Moussaid
  3. O. Gouasnouane
1
LMCSA, FSTM, Hassan II University of Casablanca
2
LMCSA, FSTM, Hassan II University of Casablanca
3
LMCSA, FSTM, Hassan II University of Casablanca

In this paper we propose a new mathematical model for joint Blind Deconvolution and Inpainting.  The main objective is the treatment of blurred images with missing parts, through the game theory framework, in particular, a Nash game, we define two players: Player 1 handles the image intensity while Player 2, operates on the blur kernel.  The two engage in a game until the equilibrium is reached.  Finally, we provide some numerical examples: we compare the efficiency of our proposed approach to other existing methods in the literature that deals with Blind Deconvolution and Inpainting separately.

blind deconvolution
inpainting
game theory
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