A comparative study of game theory techniques for blind deconvolution
| dc.citation.epage | 308 | |
| dc.citation.issue | 11 | |
| dc.citation.journalTitle | Математичне моделювання та комп'ютинг | |
| dc.citation.spage | 300 | |
| dc.citation.volume | 1 | |
| dc.contributor.affiliation | Університет Хасана ІІ Касабланки | |
| dc.contributor.affiliation | Hassan II University of Casablanca | |
| dc.contributor.author | Наср, Н. | |
| dc.contributor.author | Муссаїд, Н. | |
| dc.contributor.author | Гуасноуан, О. | |
| dc.contributor.author | Nasr, N. | |
| dc.contributor.author | Moussaid, N. | |
| dc.contributor.author | Gouasnouane, O. | |
| dc.coverage.placename | Львів | |
| dc.coverage.placename | Lviv | |
| dc.date.accessioned | 2025-10-20T07:44:21Z | |
| dc.date.created | 2024-02-24 | |
| dc.date.issued | 2024-02-24 | |
| dc.description.abstract | Мета цього дослідження полягає в тому, щоб підкреслити потенціал використання теорії ігор для роботи зі сліпою деконволюцією зображень. Розглядається статична гра двох гравців. Перший гравець контролює інтенсивність зображення, а другий гравець контролює ядро розмиття. У цій грі кожен гравець прагне мінімізувати свій власний функціонал. Результатом гри є пара стратегій: зображення з усуненням розмиття та оцінка ядра розмиття, яка мінімізує два функціонали. Визначається оптимальне зменшення розмиття зображення, використовуючи два конкретні підходи теорії ігор, нещодавно представлені: метод Неша [ 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) ] та метод розв'язання Калая-Смородинського [ Nasr N., Moussaid N., Gouasnouane O. The Kalai Smorodinsky solution for blind deconvolution. Computational and Applied Mathematics. 41 , 222 (2022) . Оцінюється продуктивність двох методів за допомогою числових експериментів та використання деяких об'єктивних показників якості. | |
| dc.description.abstract | 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. | |
| dc.format.extent | 300-308 | |
| dc.format.pages | 9 | |
| dc.identifier.citation | Nasr N. A comparative study of game theory techniques for blind deconvolution / N. Nasr, N. Moussaid, O. Gouasnouane // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 1. — No 11. — P. 300–308. | |
| dc.identifier.citationen | Nasr N. A comparative study of game theory techniques for blind deconvolution / N. Nasr, N. Moussaid, O. Gouasnouane // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 1. — No 11. — P. 300–308. | |
| dc.identifier.doi | 10.23939/mmc2024.01.300 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/113789 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Математичне моделювання та комп'ютинг, 11 (1), 2024 | |
| dc.relation.ispartof | Mathematical Modeling and Computing, 11 (1), 2024 | |
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| dc.relation.references | [14] Likas A. C., Galatsanos N. P. A variational approach for Bayesian blind image deconvolution. IEEE Transactions on Signal Processing. 52 (8), 2222–2233 (2004). | |
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| dc.relation.references | [16] Chan T. F., Wong C.-K. Total variation blind deconvolution. IEEE Transactions on Image Processing. 7 (3), 370–375 (1998). | |
| dc.relation.references | [17] You Y.-L., Kaveh M. Blind image restoration by anisotropic regularization. IEEE Transactions on Image Processing. 8 (3), 396–407 (1999). | |
| dc.relation.references | [18] Kenig T., Kam Z., Feuer A. Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 (12), 2191–2204 (2010). | |
| dc.relation.references | [19] Wang L., Huang Y., Luo X., Wang Z., Luo S. Image deblurring with filters learned by extreme learning machine. Neurocomputing. 74 (16), 2464–2474 (2011). | |
| dc.relation.references | [20] Li L., Pan J., Lai W.-S., Gao C., Sang N., Yang M.-H. Blind Image Deblurring via Deep Discriminative Priors. International Journal of Computer Vision. 127, 1025–1043 (2019). | |
| dc.relation.references | [21] Sun J., Cao W., Xu Z., Ponce J. Learning a convolutional neural network for non-uniform motion blur removal. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 769–777 (2015). | |
| dc.relation.references | [22] Elmoumen S., Moussaid N., Aboulaich R. Image retrieval using Nash equilibrium and Kalai–Smorodinsky solution. Mathematical Modeling and Computing. 8 (4), 646–657 (2021). | |
| dc.relation.references | [23] 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). | |
| dc.relation.references | [24] Nasr N., Moussaid N., Gouasnouane O. A Nash-game approach to Blind Image Deblurring. 2021 Third International Conference on Transportation and Smart Technologies (TST). 36–41 (2021). | |
| dc.relation.references | [25] Nasr N., Moussaid N., Gouasnouane O. The Kalai Smorodinsky solution for blind deconvolution. Computational and Applied Mathematics. 41, 222 (2022). | |
| dc.relation.references | [26] Das I., Dennis J. E. Normal–Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems. SIAM Journal on Optimization. 8 (3), 631–657 (2000). | |
| dc.relation.referencesen | [1] Hunt B. R. The Application of Constrained Least Squares Estimation to Image Restoration by Digital Computer. IEEE Transactions on Computers. C-22 (9), 805–812 (1973). | |
| dc.relation.referencesen | [2] Tikhonov A. N. On the stability of inverse problems. Proceedings of the USSR Academy of Sciences. 39 (5), 176–179 (1943). | |
| dc.relation.referencesen | [3] Bardsley J. M., Laobeul N. Tikhonov regularized Poisson likelihood estimation: theoretical justification and a computational method. Inverse Problems in Science and Engineering. 16 (2), 199–215 (2008). | |
| dc.relation.referencesen | [4] Rudin L. I., Osher S., Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena. 60 (1–4), 259–268 (1992). | |
| dc.relation.referencesen | [5] Rodr´ıguez P.A., Wohlberg B. Efficient Minimization Method for a Generalized Total Variation Functional. IEEE Transactions on Image Processing. 18 (2), 322–332 (2009). | |
| dc.relation.referencesen | [6] Babacan S. D., Molina R., Katsaggelos A. K. Variational Bayesian Blind Deconvolution Using a Total Variation Prior. IEEE Transactions on Image Processing. 18 (1), 12–26 (2009). | |
| dc.relation.referencesen | [7] Beck A., Teboulle M. Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems. IEEE Transactions on Image Processing. 18 (11), 2419–2434 (2009). | |
| dc.relation.referencesen | [8] Chan R. H., Tao M., Yuan X. Constrained Total Variation Deblurring Models and Fast Algorithms Based on Alternating Direction Method of Multipliers. SIAM Journal on Imaging Sciences. 6 (1), 680–697 (2013). | |
| dc.relation.referencesen | [9] Dong W., Zhang L., Shi G., Wu X. Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization. IEEE Transactions on Image Processing. 20 (7), 1838–1857 (2011). | |
| dc.relation.referencesen | [10] Elad M. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. New York, NY, USA, Springer (2010). | |
| dc.relation.referencesen | [11] Lagendijk R. L., Tekalp A. M., Biemond J. Maximum likelihood image and blur identification: a unifying approach. Optical Engineering. 29 (5), 422–435 (1990). | |
| dc.relation.referencesen | [12] Levin A., Weiss Y., Durand F., Freeman W. T. Understanding and evaluating blind deconvolution algorithms. 2009 IEEE Conference on Computer Vision and Pattern Recognition. 1964–1971 (2009). | |
| dc.relation.referencesen | [13] Levin A., Weiss Y., Durand F., Freeman W. T. Efficient marginal likelihood optimization in blind deconvolution. CVPR 2011. 2657–2664 (2011). | |
| dc.relation.referencesen | [14] Likas A. C., Galatsanos N. P. A variational approach for Bayesian blind image deconvolution. IEEE Transactions on Signal Processing. 52 (8), 2222–2233 (2004). | |
| dc.relation.referencesen | [15] Green P. J. Bayesian reconstructions from emission tomography data using a modified EM algorithm. IEEE Transactions on Medical Imaging. 9 (1), 84–93 (1990). | |
| dc.relation.referencesen | [16] Chan T. F., Wong C.-K. Total variation blind deconvolution. IEEE Transactions on Image Processing. 7 (3), 370–375 (1998). | |
| dc.relation.referencesen | [17] You Y.-L., Kaveh M. Blind image restoration by anisotropic regularization. IEEE Transactions on Image Processing. 8 (3), 396–407 (1999). | |
| dc.relation.referencesen | [18] Kenig T., Kam Z., Feuer A. Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 (12), 2191–2204 (2010). | |
| dc.relation.referencesen | [19] Wang L., Huang Y., Luo X., Wang Z., Luo S. Image deblurring with filters learned by extreme learning machine. Neurocomputing. 74 (16), 2464–2474 (2011). | |
| dc.relation.referencesen | [20] Li L., Pan J., Lai W.-S., Gao C., Sang N., Yang M.-H. Blind Image Deblurring via Deep Discriminative Priors. International Journal of Computer Vision. 127, 1025–1043 (2019). | |
| dc.relation.referencesen | [21] Sun J., Cao W., Xu Z., Ponce J. Learning a convolutional neural network for non-uniform motion blur removal. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 769–777 (2015). | |
| dc.relation.referencesen | [22] Elmoumen S., Moussaid N., Aboulaich R. Image retrieval using Nash equilibrium and Kalai–Smorodinsky solution. Mathematical Modeling and Computing. 8 (4), 646–657 (2021). | |
| dc.relation.referencesen | [23] 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). | |
| dc.relation.referencesen | [24] Nasr N., Moussaid N., Gouasnouane O. A Nash-game approach to Blind Image Deblurring. 2021 Third International Conference on Transportation and Smart Technologies (TST). 36–41 (2021). | |
| dc.relation.referencesen | [25] Nasr N., Moussaid N., Gouasnouane O. The Kalai Smorodinsky solution for blind deconvolution. Computational and Applied Mathematics. 41, 222 (2022). | |
| dc.relation.referencesen | [26] Das I., Dennis J. E. Normal–Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems. SIAM Journal on Optimization. 8 (3), 631–657 (2000). | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2024 | |
| dc.subject | зменшення розмитості | |
| dc.subject | теорія ігор | |
| dc.subject | багатоцільова оптимізація | |
| dc.subject | deblurring | |
| dc.subject | game theory | |
| dc.subject | multi-objective optimization | |
| dc.title | A comparative study of game theory techniques for blind deconvolution | |
| dc.title.alternative | Порівняльне дослідження методів теорії ігор для сліпої деконволюції | |
| dc.type | Article |
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