The Kalai Smorodinsky solution for blind deconvolution

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.

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A game theory approach for joint blind deconvolution and inpainting

Nasr¹,

Moussaid²,

Gouasnouane³

2023

Math. Model. Comput.

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Searching for similar images using Nash game and machine learning

Semmane,

Moussaid,

Ziani

2024

Math. Model. Comput.

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The storage of large amounts of digital data, as well as the processing of digital images, are currently expanding significantly across a range of application areas. As a result, effective management of big images databases is necessary, which calls for the employment of automated and cutting-edge indexing techniques. One method used for this is Content-Based Image Retrieval (CBIR), which tries to index and query the picture database using visual aspects of the image rather than its semantic features. In this article, we propose to explore a digital search engine for similar images, based on multiple image representations and clustering, improved by game theory and machine learning methods.

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A comparative study of game theory techniques for blind deconvolution

Nasr,

Moussaid,

Gouasnouane

2024

Math. Model. Comput.

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The aim of this study is to lay emphasis on the potential of the use of Game theory to deal with Blind image Deconvolution. We consider a static game of two players. Player one controls the image intensity while the player two controls the blur kernel. In this game each player aims at minimizing his own functional. The outcome of the game is a pair of strategies: a deblurred image and an estimation of the blur kernel, that minimizes two functionals. We determine the optimal image deblurring using two particular game theoretic approaches, recently introduced: the Nash method [Meskine D., Moussaid N., Berhich S. Blind image deblurring by game theory. Proceedings of the 2nd International Conference on Networking, Information Systems & Security (NISS '19). 31 (2019)] and the Kalai–Smorodinsky solution method [Nasr N., Moussaid N., Gouasnouane O. The Kalai Smorodinsky solution for blind deconvolution. Computational and Applied Mathematics. 41, 222 (2022)]. We evaluate the performance of two techniques through numerical experiments and using some objective quality metrics.

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The Kalai Smorodinsky solution for blind deconvolution

Cited by 4 publications

References 21 publications

A game theory approach for joint blind deconvolution and inpainting

A game theory approach for joint blind deconvolution and inpainting

Searching for similar images using Nash game and machine learning

A comparative study of game theory techniques for blind deconvolution

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