Blind image deblurring by game theory

In this paper, we present a novel blind deconvolution technique for the restoration of linearly degraded images without explicit knowledge of either the original image or the point spread function (PSF). We propose to determine the optimal image deblurring as a Nash equilibrium, we use two criteria associated with two players. In this case, we suggest applying a concurrent optimization realized by an algorithm simulating a Nash game between two the players. Indeed, The aim of the first player is to minimize objective function using the first strategy image deblurring. Furthermore, a point spread function (PSF) is used by the second player. Accordingly, we present a mathematical proof of the existence of a discrete valued optimal solution for the second player, and it is concluded that no regularization of the sub optimization problem is needed. Finally, We present some numerical examples which illustrate the proposed methodology.