A new mathematical model for contrast enhancement in digital images
| dc.citation.epage | 350 | |
| dc.citation.issue | 2 | |
| dc.citation.journalTitle | Математичне моделювання та комп'ютинг | |
| dc.citation.spage | 342 | |
| dc.contributor.affiliation | Університет Каді Айяда | |
| dc.contributor.affiliation | Університет Мухаммеда Першого | |
| dc.contributor.affiliation | Перший університет Хасана | |
| dc.contributor.affiliation | Cadi Ayyad University | |
| dc.contributor.affiliation | University of Mohammed First | |
| dc.contributor.affiliation | Hassan First University | |
| dc.contributor.author | Алаа, Н. Е. | |
| dc.contributor.author | Алаа, К. | |
| dc.contributor.author | Атунті, М. | |
| dc.contributor.author | Акель, Ф. | |
| dc.contributor.author | Alaa, N. E. | |
| dc.contributor.author | Alaa, K. | |
| dc.contributor.author | Atounti, M. | |
| dc.contributor.author | Aqel, F. | |
| dc.coverage.placename | Львів | |
| dc.coverage.placename | Lviv | |
| dc.date.accessioned | 2025-03-04T11:14:22Z | |
| dc.date.created | 2022-02-28 | |
| dc.date.issued | 2022-02-28 | |
| dc.description.abstract | Метою даної роботи є запропонування нової математичної моделі для оптимального підвищення контрастності цифрового зображення. Основна ідея полягає в поєднанні стратегії “розділяй і володарюй” та реакційно–дифузійної математичної моделі для підвищення контрасту і виділення інформації та деталей зображення на основі нової концепції алгоритму синусно-косинусної оптимізації. Техніка “розділяй і володарюй” є підходящим методом для посилення контрасту з ефективністю, яка безпосередньо залежить від вибору ваг у підпросторах декомпозиції. Методи: у цій роботі використано новий алгоритм для оптимального вибору ваг з урахуванням оптимізації міри покращення (EME). Результати: для оцінки ефективності запропонованого алгоритму подано експериментальні результати, які показують, що запропонована методика гібридизації є надійно ефективною і дає чіткі та висококонтрастні зображення. | |
| dc.description.abstract | The aim of this work is to propose a new mathematical model for optimal contrast enhancement of a digital image. The main idea is to combine the Divide-and-Conquer strategy, and a reaction diffusion mathematical model to enhance the contrast, and highlight the information and details of the image, based on a new conception of the Sine-Cosine optimization algorithm. The Divide-and-Conquer technique is a suitable method for contrast enhancement with an efficiency that directly depends on the choice of weights in the decomposition subspaces. Methods: in this paper, a new algorithm has been used for the optimal selection of the weights considering the optimization of the enhancement measure (EME). Results: in order to evaluate the effectiveness of the proposed algorithm, experimental results are presented which show that the proposed hybridization technique is robustly effective and produces clear and high contrast images. | |
| dc.format.extent | 342-350 | |
| dc.format.pages | 9 | |
| dc.identifier.citation | A new mathematical model for contrast enhancement in digital images / N. E. Alaa, K. Alaa, M. Atounti, F. Aqel // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 9. — No 2. — P. 342–350. | |
| dc.identifier.citationen | A new mathematical model for contrast enhancement in digital images / N. E. Alaa, K. Alaa, M. Atounti, F. Aqel // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 9. — No 2. — P. 342–350. | |
| dc.identifier.doi | doi.org/10.23939/mmc2022.02.342 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/63435 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Математичне моделювання та комп'ютинг, 2 (9), 2022 | |
| dc.relation.ispartof | Mathematical Modeling and Computing, 2 (9), 2022 | |
| dc.relation.references | [1] Zhang Y., Gong S., Luo M. Image quality guided biology application for genetic analysis. Journal of Visual Communication and Image Representation. 64, 102606 (2019). | |
| dc.relation.references | [2] Hum Y. C., Lai K. W., Mohamed Salim M. I. Multiobjectives bihistogram equalization for image contrast enhancement. Complexity. 20 (2), 22–36 (2014). | |
| dc.relation.references | [3] Wang C., Ma K.-K. Feature histogram equalization for feature contrast enhancement. Journal of Visual Communication and Image Representation. 26, 255–264 (2015). | |
| dc.relation.references | [4] Gonzalez R. C., Woods R. E. Digital image processing. Pearson Prentice Hall: Upper Saddle River, NJ (2002). | |
| dc.relation.references | [5] Demirel H., Ozcinar C., Anbarjafari G. Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE Geoscience and remote sensing letters. 7 (2), 333–337 (2009). | |
| dc.relation.references | [6] Fu X., Wang J., Zeng D., Huang Y., Ding X. Remote sensing image enhancement using regularizedhistogram equalization and DCT. IEEE Geoscience and Remote Sensing Letters. 12 (11), 2301–2305 (2015). | |
| dc.relation.references | [7] Shan Q., Jia J., Brown M. S. Globally optimized linear windowed tone mapping. IEEE transactions on visualization and computer graphics. 16 (4), 663–675 (2009). | |
| dc.relation.references | [8] Joshi S. H., Marquina A., Osher S. J., Dinov I., Van Horn J. D., Toga A. W. MRI resolution enhancement using total variation regularization. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 161–164 (2009). | |
| dc.relation.references | [9] Zhang X., Chan K. L., Constable M. Atmospheric perspective effect enhancement of landscape photographs through depth-aware contrast manipulation. IEEE Trans. Multimedia. 16 (3), 653–667 (2014). | |
| dc.relation.references | [10] Micheloni C., Foresti G. L. Image acquisition enhancement for active video surveillance. Proceedings of the 17th International Conference on Pattern Recognition. 3, 326–329 (2004). | |
| dc.relation.references | [11] Elad M., Kimmel R., Shaked D., Keshet R. Reduced complexity Retinex algorithm via the variational approach. Journal of visual communication and image representation. 14 (4), 369–388 (2003). | |
| dc.relation.references | [12] Ng M. K., Wang W. A total variation model for Retinex. SIAM Journal on Imaging Sciences. 4 (1), 345-365 (2011). | |
| dc.relation.references | [13] Wang L., Xiao L., Liu H., Wei Z. Variational Bayesian method for Retinex. IEEE Transactions on Image Processing. 23 (8), 3381–3396 (2014). | |
| dc.relation.references | [14] Polesel A., Ramponi G., Mathews V. G. Image enhancement via adaptive unsharp masking. IEEE Transactions on Image Processing. 9 (3), 505–510 (2000). | |
| dc.relation.references | [15] Deng G. A generalized unsharp masking algorithm. IEEE transactions on Image Processing. 20 (5), 1249–1261 (2010). | |
| dc.relation.references | [16] Farbman Z., Fattal R., Lischinski D., Szeliski R. Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics. 27 (3), 1–10 (2008). | |
| dc.relation.references | [17] Wang W., Chen C., Ng M. K. An image pixel based variational model for histogram equalization. Journal of Visual Communication and Image Representation. 34, 118–134 (2016). | |
| dc.relation.references | [18] Stout Q. F. Supporting divide-and-conquer algorithms for image processing. Journal of Parallel and Distributed Computing. 4 (1), 95–115 (1987). | |
| dc.relation.references | [19] Bacquey N. The packing problem: A divide and conquer algorithm on cellular automata. Automata & JAC 2012. 1–10 (2012). | |
| dc.relation.references | [20] Zhuang P., Fu X., Huang Y., Ding X. Image enhancement using divide-and-conquer strategy. Journal of Visual Communication and Image Representation. 45, 137–146 (2017). | |
| dc.relation.references | [21] Alaa K., Zirhem M., Atounti M. Nonlinear reaction-diffusion mathematical model and divide-conquer technique for Image Restoration and Contrast Enhancement. Mathematical Modeling and Computing. 8 (3), 549–559 (2021). | |
| dc.relation.references | [22] Zhuang P., Ding X. Divide-and-conquer framework for image restoration and enhancement. Engineering Applications of Artificial Intelligence. 85, 830–844 (2019). | |
| dc.relation.references | [23] Liu D. N., Hou R., Wu W. Z., Hua J. W., Wang X. Y., Pang B. Research on infrared image enhancement and segmentation of power equipment based on partial differential equation. Journal of Visual Communication and Image Representation. 64, 102610 (2019). | |
| dc.relation.references | [24] FitzHugh R. Impulses and physiological states in theoretical models of nerve membrane. Biophysical journal. 1 (6), 445–466 (1961). | |
| dc.relation.references | [25] Nagumo J., Arimoto S., Yoshizawa S. An active pulse transmission line simulating nerve axon. Proceedings of the IRE. 50 (10), 2061–2070 (1962). | |
| dc.relation.references | [26] Ambrosio B., Aziz-Alaoui M. A. Synchronization and control of coupled reaction-diffusion systems of the FitzHugh–Nagumo type Computers and Mathematics with ApplicationsOpen Access. 64 (5), 934–943 (2012). | |
| dc.relation.references | [27] Alaa N., Zirhem M. Bio-inspired reaction diffusion system applied to image restoration. International Journal of Bio-Inspired Computation. 12 (2), 128–137 (2018). | |
| dc.relation.references | [28] Li W., Shi M., Ogunbona P. A new divide and conquer Algorithm for graph-based image and video segmentation. 2005 IEEE 7th Workshop on Multimedia Signal Processing. 1–4 (2005). | |
| dc.relation.references | [29] K¨orting T. S., Castejon E. F., Fonseca L. M. G. The divide and segment method for parallel image segmentation. International Conference on Advanced Concepts for Intelligent Vision Systems. 504–515 (2013). | |
| dc.relation.references | [30] Zirhem M., Alaa N. Texture synthesis by reaction diffusion process. Annals of the University of Craiova, Mathematics and Computer Science Series. 42 (1), 56–69 (2015). | |
| dc.relation.references | [31] Alaa N., Zirhem M. Entropy solution for a fourth-order nonlinear degenerate problem for image decomposition. Journal of Advanced Mathematical Studies. 11, 412–427 (2018). | |
| dc.relation.references | [32] Mirjalili S. SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems. 96, 120–133 (2016). | |
| dc.relation.referencesen | [1] Zhang Y., Gong S., Luo M. Image quality guided biology application for genetic analysis. Journal of Visual Communication and Image Representation. 64, 102606 (2019). | |
| dc.relation.referencesen | [2] Hum Y. C., Lai K. W., Mohamed Salim M. I. Multiobjectives bihistogram equalization for image contrast enhancement. Complexity. 20 (2), 22–36 (2014). | |
| dc.relation.referencesen | [3] Wang C., Ma K.-K. Feature histogram equalization for feature contrast enhancement. Journal of Visual Communication and Image Representation. 26, 255–264 (2015). | |
| dc.relation.referencesen | [4] Gonzalez R. C., Woods R. E. Digital image processing. Pearson Prentice Hall: Upper Saddle River, NJ (2002). | |
| dc.relation.referencesen | [5] Demirel H., Ozcinar C., Anbarjafari G. Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE Geoscience and remote sensing letters. 7 (2), 333–337 (2009). | |
| dc.relation.referencesen | [6] Fu X., Wang J., Zeng D., Huang Y., Ding X. Remote sensing image enhancement using regularizedhistogram equalization and DCT. IEEE Geoscience and Remote Sensing Letters. 12 (11), 2301–2305 (2015). | |
| dc.relation.referencesen | [7] Shan Q., Jia J., Brown M. S. Globally optimized linear windowed tone mapping. IEEE transactions on visualization and computer graphics. 16 (4), 663–675 (2009). | |
| dc.relation.referencesen | [8] Joshi S. H., Marquina A., Osher S. J., Dinov I., Van Horn J. D., Toga A. W. MRI resolution enhancement using total variation regularization. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 161–164 (2009). | |
| dc.relation.referencesen | [9] Zhang X., Chan K. L., Constable M. Atmospheric perspective effect enhancement of landscape photographs through depth-aware contrast manipulation. IEEE Trans. Multimedia. 16 (3), 653–667 (2014). | |
| dc.relation.referencesen | [10] Micheloni C., Foresti G. L. Image acquisition enhancement for active video surveillance. Proceedings of the 17th International Conference on Pattern Recognition. 3, 326–329 (2004). | |
| dc.relation.referencesen | [11] Elad M., Kimmel R., Shaked D., Keshet R. Reduced complexity Retinex algorithm via the variational approach. Journal of visual communication and image representation. 14 (4), 369–388 (2003). | |
| dc.relation.referencesen | [12] Ng M. K., Wang W. A total variation model for Retinex. SIAM Journal on Imaging Sciences. 4 (1), 345-365 (2011). | |
| dc.relation.referencesen | [13] Wang L., Xiao L., Liu H., Wei Z. Variational Bayesian method for Retinex. IEEE Transactions on Image Processing. 23 (8), 3381–3396 (2014). | |
| dc.relation.referencesen | [14] Polesel A., Ramponi G., Mathews V. G. Image enhancement via adaptive unsharp masking. IEEE Transactions on Image Processing. 9 (3), 505–510 (2000). | |
| dc.relation.referencesen | [15] Deng G. A generalized unsharp masking algorithm. IEEE transactions on Image Processing. 20 (5), 1249–1261 (2010). | |
| dc.relation.referencesen | [16] Farbman Z., Fattal R., Lischinski D., Szeliski R. Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics. 27 (3), 1–10 (2008). | |
| dc.relation.referencesen | [17] Wang W., Chen C., Ng M. K. An image pixel based variational model for histogram equalization. Journal of Visual Communication and Image Representation. 34, 118–134 (2016). | |
| dc.relation.referencesen | [18] Stout Q. F. Supporting divide-and-conquer algorithms for image processing. Journal of Parallel and Distributed Computing. 4 (1), 95–115 (1987). | |
| dc.relation.referencesen | [19] Bacquey N. The packing problem: A divide and conquer algorithm on cellular automata. Automata & JAC 2012. 1–10 (2012). | |
| dc.relation.referencesen | [20] Zhuang P., Fu X., Huang Y., Ding X. Image enhancement using divide-and-conquer strategy. Journal of Visual Communication and Image Representation. 45, 137–146 (2017). | |
| dc.relation.referencesen | [21] Alaa K., Zirhem M., Atounti M. Nonlinear reaction-diffusion mathematical model and divide-conquer technique for Image Restoration and Contrast Enhancement. Mathematical Modeling and Computing. 8 (3), 549–559 (2021). | |
| dc.relation.referencesen | [22] Zhuang P., Ding X. Divide-and-conquer framework for image restoration and enhancement. Engineering Applications of Artificial Intelligence. 85, 830–844 (2019). | |
| dc.relation.referencesen | [23] Liu D. N., Hou R., Wu W. Z., Hua J. W., Wang X. Y., Pang B. Research on infrared image enhancement and segmentation of power equipment based on partial differential equation. Journal of Visual Communication and Image Representation. 64, 102610 (2019). | |
| dc.relation.referencesen | [24] FitzHugh R. Impulses and physiological states in theoretical models of nerve membrane. Biophysical journal. 1 (6), 445–466 (1961). | |
| dc.relation.referencesen | [25] Nagumo J., Arimoto S., Yoshizawa S. An active pulse transmission line simulating nerve axon. Proceedings of the IRE. 50 (10), 2061–2070 (1962). | |
| dc.relation.referencesen | [26] Ambrosio B., Aziz-Alaoui M. A. Synchronization and control of coupled reaction-diffusion systems of the FitzHugh–Nagumo type Computers and Mathematics with ApplicationsOpen Access. 64 (5), 934–943 (2012). | |
| dc.relation.referencesen | [27] Alaa N., Zirhem M. Bio-inspired reaction diffusion system applied to image restoration. International Journal of Bio-Inspired Computation. 12 (2), 128–137 (2018). | |
| dc.relation.referencesen | [28] Li W., Shi M., Ogunbona P. A new divide and conquer Algorithm for graph-based image and video segmentation. 2005 IEEE 7th Workshop on Multimedia Signal Processing. 1–4 (2005). | |
| dc.relation.referencesen | [29] K¨orting T. S., Castejon E. F., Fonseca L. M. G. The divide and segment method for parallel image segmentation. International Conference on Advanced Concepts for Intelligent Vision Systems. 504–515 (2013). | |
| dc.relation.referencesen | [30] Zirhem M., Alaa N. Texture synthesis by reaction diffusion process. Annals of the University of Craiova, Mathematics and Computer Science Series. 42 (1), 56–69 (2015). | |
| dc.relation.referencesen | [31] Alaa N., Zirhem M. Entropy solution for a fourth-order nonlinear degenerate problem for image decomposition. Journal of Advanced Mathematical Studies. 11, 412–427 (2018). | |
| dc.relation.referencesen | [32] Mirjalili S. SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems. 96, 120–133 (2016). | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2022 | |
| dc.subject | реакція | |
| dc.subject | дифузія | |
| dc.subject | посилення контрасту | |
| dc.subject | техніка “розділяй і володарюй” | |
| dc.subject | алгоритм синус-косинус | |
| dc.subject | алгоритм оптимізації | |
| dc.subject | математична модель ФітцХью–Нагумо | |
| dc.subject | reaction diffusion | |
| dc.subject | contrast enhancement | |
| dc.subject | Divide-and-Conquer technique | |
| dc.subject | Sine-Cosine algorithm | |
| dc.subject | optimization algorithm | |
| dc.subject | FitzHugh–Nagumo mathematical model | |
| dc.title | A new mathematical model for contrast enhancement in digital images | |
| dc.title.alternative | Нова математична модель для підвищення контрастності цифрових зображень | |
| dc.type | Article |
Files
License bundle
1 - 1 of 1