blind deconvolution

Towards a Nash game strategy approach to blind image deconvolution: a fractional-order derivative variational framework

Image restoration is a critical process aimed at recovering degraded images, often impacted by factors including motion blur, sensor blurring, defocused photography, optical aberrations, atmospheric distortions, and noise-induced blur.  The inherent challenge lies in the typical scenario where both the original image and the blur kernel (Point Spread Function, PSF) are unknown.  This restorative process finds applications in various fields, including sensing, medical imaging, astronomy, remote sensing, and criminal investigations.  This paper introduces an innovative ap

A game theory approach for joint blind deconvolution and inpainting

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 separa