Treffer: A Nash game for blind image deconvolution based on fractional-order derivatives.

This study extends our previous work on image deconvolution, where we modelled the process as a two-player static game. In the initial work, and without prior knowledge of the original image or the point spread function, player one aimed to recover a clean image, while player two estimated the point spread function using fractional-order derivatives. In this extended version, both players define their objective functions using fractional-order derivatives, leading to a more robust approach. We prove the existence of a Nash equilibrium for this game and introduce an alternating optimization algorithm to ensure convergence. Extensive experiments show that the proposed method outperforms our previous work and other techniques, with significant improvements in image quality metrics such as Peak Signal-to-Noise Ratio and Structural Similarity Index Measure. This enhanced framework offers a novel and effective approach to the challenging problem of blind image deconvolution in image processing. [ABSTRACT FROM AUTHOR]