About the Journal

Proceedings of the YSU A: Physical and Mathematical Sciences aims to publish original research papers and survey articles in all areas of physics, mathematics and informatics. Proc. YSU A: Phys. Math. Sci. accepts also review articles, short communications, conference proceedings, algorithms, Ph.D and doctoral thesis’s and other items with a detailed exposition of results, proofs, experiments and examples. One of purposes is to reflect the progress of the research in all areas of physics, mathematics and informatics in Armenia and, by providing an international forum, to stimulate its further developments. 

Current Issue

Vol. 60 No. 1 (269) (2026)
DOI: https://doi.org/10.46991/PYSUA.2026.60.1
Published: 2026-04-30

Mathematics

  • Mathematics

    HERMITE MULTIVARIATE INTERPOLATION FORMULA

    Hakop A. Hakopian
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    Abstract

    We present a new formula for the Hermite multivariate interpolation problem in the framework of the Chung--Yao approach.

    References

    Chung K.C., Yao T.H. On Latticies Admitting Unique Lagrange Interpolations. SIAM J. Numer. Anal. 14 (1977), 735-753. https://doi.org/10.1137/0714050

    Hakopian H.A. Multivariate Splie-functions, B-spline Bases and Polynomial Interpolations II. Studia Math. 79 (1984), 91-102.

  • Mathematics

    NEAR-INTERVAL EDGE-COLORINGS OF COMPLETE BIPARTITE GRAPHS

    Petros A. Petrosyan, Vahagn D. Tsirunyan
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    Abstract

    A proper edge-coloring of a graph $G$ is a mapping $\alpha:E(G)\longrightarrow  \mathbb{N}$ such that $\alpha(e)\not=\alpha(e')$ for every pair of adjacent edges $e$ and $e'$ in $G$. If $\alpha $ is a proper edge-coloring of $G$ and $v\in V(G)$, then $S_{G}\left(v,\alpha \right)$ denotes the set of colors appearing on edges incident to $v$. A proper edge-coloring $\alpha$ of a graph $G$ with colors $1,\ldots,t$ is called a near-interval $t$-coloring if all colors are used, and for each vertex $v\in V(G)$, $S_G(v,\alpha)$ is an interval of integers with no more than one gap.  If a graph $G$ has such a coloring, the minimum number of colors in a near-interval coloring of a graph $G$ is denoted by $w^{1}(G)$. It is known that all complete bipartite graphs admit near-interval colorings. In this paper, we determine or bound the parameter $w^{1}(K_{m,n})$ ($m,n\in\mathbb{N}$) for complete bipartite graphs.

    References

    Asratian A.S., Denley T.M.J., Haggkvist R. Bipartite Graphs and their Applications. Cambridge University Press, Cambridge (1998). https://doi.org/10.1017/CBO9780511984068

    West D.B. Introduction to Graph Theory. New Jersey, Prentice-Hall (2001).

    Petrosyan P.A., Arakelyan H.Z., Baghdasaryan V.M. A Generalization of Interval Edge-colorings of Graphs. Discrete Appl. Math. 158 (16) (2010), 1827-1837. https://doi.org/10.1016/j.dam.2010.06.012

    Petrosyan P.A., Arakelyan Z. On a Generalization of Interval Edge Colorings of Graphs. Mathematical Problems of Computer Science, 29 (2007), 26-32.

    Asratian A.S., Kamalian R.R. Interval Colorings of Edges of a Multigraph. Appl. Math. 5 (1987), 25-34 (in Russian).

    Asratian A.S., Kamalian R.R. Investigation on Interval Edge-Colorings of Graphs. J. Combin. Theory Ser. B 62 (1994), 34-43. https://doi.org/10.1006/jctb.1994.1053

    Jensen T.R., Toft B. Graph Coloring Problems. Wiley, Wiley Series in Discrete Mathematics and Optimization (2011).

    Kubale M. Graph Colorings. American Mathematical Society (2004).

    Stiebitz M., Scheide D., Toft B., Favrholdt L.M. Graph Edge Coloring: Vizing's Theorem and Goldberg's Conjecture, Wiley (2012).

    Petrosyan P.A., Khachatrian H.H., Mamikonyan T.K. On Interval Edge-Colorings of Bipartite Graphs. IEEE Computer Science and Information Technologies (CSIT) (2015), 71-76. https://doi.org/10.1109/CSITechnol.2015.7358253

    Casselgren C.J., Toft B. On Interval Edge Colorings of Biregular Bipartite Graphs with Small Vertex Degrees. J. Graph Theory 80 (2) (2015), 83-97. https://doi.org/10.1002/jgt.21841

    Casselgren C.J., Malafiejski M., Pastuszak K., Petrosyan P.A. Near-interval Edge Colorings of Graphs. Discrete Appl. Math. 372 (2025), 23-36. https://doi.org/10.1016/j.dam.2025.03.011

    Kamalian R.R. Interval Colorings of Complete Bipartite Graphs and Trees. Preprint. Comp. Cen. of Acad. Sci. of Armenian SSR. Yerevan (1989) (in Russian).

  • Mathematics

    ON SUM EDGE-COLORINGS OF SOME REGULAR GRAPHS

    Hamlet V. Mikaelyan
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    Abstract

    A sum edge-coloring of a graph is an assignment of positive integers to the edges of the graph, so that adjacent edges correspond to different numbers (colors) and the sum of the numbers on all the edges is minimum possible. This minimum possible sum is called the edge-chromatic sum of the graph, and the minimal number of colors needed for a sum edge-coloring is called the edge-strength of the graph. In this paper, we give the exact values of the edge-chromatic sums and edge-strengths for cycle powers and generalized cycles.

    References

    West D.B. Introduction to Graph Theory. Prentice-Hall, New Jersey (2001). https://dwest.web.illinois.edu/igt/

    Bar-Noy A., Bellare M., et al. On Chromatic Sums and Distributed Resource Allocation. Inform. and Comput. 140 (1998), 183-202. https://doi.org/10.1006/inco.1997.2677

    Salavatipour M.R. On Sum Coloring of Graphs. Discrete Appl. Math. 127 (2003), 477-488. https://doi.org/10.1016/S0166-218X(02)00249-4

    Giaro K., Kubale M. Edge-Chromatic Sum of Trees and Bounded Cyclicity Graphs. Inform. Process. Lett. 75 (2000), 65-69. https://doi.org/10.1016/S0020-0190(00)00072-7

    Petrosyan P.A., Kamalian R.R. On Sum Edge-Coloring of Regular, Bipartite and Split Graphs. Discrete Appl. Math. 165 (2014), 263-269. https://doi.org/10.1016/j.dam.2013.09.025

    Hajiabolhassan H., Mehrabadi M.L., Tusserkani R. Minimal Coloring and Strength of Graphs. Discrete Math. 215 (2000), 265-270. https://doi.org/10.1016/S0012-365X(99)00319-2

    Parker E.T. Edge Coloring Numbers of Some Regular Graphs. Proc. of the American Math. Society 37 (1973), 623-624. https://doi.org/10.1090/S0002-9939-1973-0313103-0

    Meidanis J. Edge Coloring of Cycle Powers is Easy. Unpublished Manuscript (1998). Available at www.ic.unicamp.br/~meidanis/research/edge/cpowers.pdf

    Mikaelyan H.V. On Sum Edge-colorings of Complete Tripartite Graphs. Proc. of the YSU A. Phys. and Math. Sci. 59 (2025), 69-83. https://doi.org/10.46991/PYSUA.2025.59.3.069

    Bermond J.-C., Favaron O., Maheo M. Hamiltonian Decomposition of Cayley Graphs of Degree 4. J. Combin. Theory Ser. B 46 (1989), 142-153. https://doi.org/10.1016/0095-8956(89)90040-3

  • Mathematics

    FINITE-DIFFRENCE STOCHASTIC SCHEMES FOR MINIMIZING A STRONGLY QUASICONVEX NON-DIFFERENTIABLE FUNCTION ON $\mathbb{R}^n$: THE NURMINSKII METHOD

    Rafik A. Khachatryan, Zohrab B. Hovhannisyan
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    Abstract

    In this work, stochastic approximation methods based on finite differences are investigated for the problem of minimizing quasiconvex functions. The main result of this work is the derivation of convergence rate estimates for stochastic finite-difference methods in the case of quasiconvex functions. The obtained results significantly extend the applicability of stochastic finite-difference methods to nonsmooth quasiconvex optimization problems and provide a rigorous justification for their use in black-box settings where the oracle returns only function values.

    References

    Lara F. On Strongly Quasiconvex Functions: Existence Results and Proximal Point Algorithm. JOTA 192 (2022), 891-911. https://doi.org/10.1007/s10957-021-01996-8

    Lara F., Marcavillaca R.T., Yuong P.T. Characterizations, Dynamical Systems and Gradient Methods for Strongly Quasiconvex Functions. (2024). https://doi.org/10.48550/arXiv.2410.03534

    Mikhalevich V.S., Gupal A.M., Norkin V.I. Methods of Nonconvex Optimization. Moscow, Nauka (1983).

    Nesterov Yu.E. Minimization Methods for Non-smooth Convex and Quasiconvex Functions. Matekon 29 (1984), 519-531.

    Shamir O., Zhang T. Stochastic Gradient Descent for Non-smooth Optimization. (2012). https://doi.org/10.48550/arXiv.1212.1824

    Gupal A.M. Algorithms for Finding the Extremum of Nondifferentiable Functions with Constraint. Kyiv, Institute of Cybernetics, Preprint (1976).

    Nesterov Yu.E., Spokoiny V. Random Gradient-Free Minimization of Convex Functions. Foundations of Computational Mathematics 17 (2017), 527-566. https://doi.org/10.1007/s10208-015-9296-2

    Polyak B.T. Existence Theorems and Convergence of Minimizing Sequences in Extremum Problems with Restrictions. Soviet Math. Dokl. 7 (1966), 72-75.

    Jovanovic M.V. A Note on Strongly Convex and Quasiconvex Functions. Math. Notes 60 (1996), 584-585. https://doi.org/10.1007/BF02309176

    Jovanovic M.V. Strongly Quasiconvex Quadratic Functions. Publications de l'institut Mathematique Nouvelle Serie 53 (1993), 153-156.

    Hazan E. Introduction to Online Convex Optimization. Essential Knowledge, Boston-Delt (2016).

    Grad S.M., Lara F., Marcavillaca R.T. Strongly Quasiconvex Functions, What We Know (So Far). (2024). https://doi.org/10.48550/arXiv.2410.23055

    Nurminskii E.A. Numerical Methods for Solving Deterministic and Stochastic Minimax Problems. Kiev, Naukova Dumka (1979).

    Robbins H., Siegmund D. A Convergence Rate Theorem for Non Negative Almost Supermartingales and Some Applications. Optimizing Methods in Statistics 8 (1971), 233-257. https://doi.org/10.1016/B978-0-12-604550-5.50015-8

Mechanics

  • Mechanics

    ADHESIVE CONTACT PROBLEM FOR AN INFINITE SHEET STRENGTHENED BY THREE PARALLEL FINITE-LENGTH STRINGERS

    Aghasi V. Kerobyan
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    Abstract

    The article considers the problem for an elastic infinite sheet, which along of three parallel lines of its upper surface is strengthened by three parallel finite-length stringers having different elastic properties. The interaction between infinite sheet and three parallel stringers takes place through thin elastic adhesive layers having other physical-mechanical properties and geometric configuration. The stringers are deformed under the action of horizontal concentrated forces, which are applied at one end points of the stringers.  The problem of determining the law of distribution of unknown contact forces acting between infinite sheet and stringers is reduced to the system of Fredholm integral equations of second kind with respect to three  unknown functions, which are specified on three parallel finite intervals. Further, are determined of the change regions of the problem characteristic parameters, for which this system of integral equations allows the exact solutions and which can be solved by the method of successive approximations. Some special cases are considered and the character and behavior of unknown shear forces near the end points of the stringers are investigated. For these cases numerical results depending on the multiparameters of the problem are investigated in the article by A.V. Kerobyan and K.P. Sahakyan (Proc. YSU. Phys. Math. Sci., 57 (3) (2023), 86-100).

    References

    Kerobyan A.V. On a Problem for an Elastic Infinite Sheet Strengthened by Two Parallel Stringers with Finite Lengths through Adhesive Shear Layers. Proc. of the YSU A. Phys. Math. Sci. 54 (2020), 153-164. https://doi.org/10.46991/PYSU:A/2020.54.3.153

    Kerobyan A.V., Sahakyan K.P. Transfer of Loads from Three Heterogeneous Elastic Stringers with Finite Lengths to an Infinite Sheet through Adhesive Layers. Proc. of the YSU A. Phys. Math. Sci. 57 (2023), 86-100. https://doi.org/10.46991/PYSU:A/2023.57.3.086

    Kerobyan A.V. Loads Transfer from the Systems of Finite Number Finite Length Stringers to an Infinite Sheet through Adhesive Layers. Proc. of the YSU A. Phys. Math. Sci. 59 (2025), 46-62. https://doi.org/10.46991/PYSU:A/2025.59.2.046

    Kerobyan A.V. About Contact Problems for an Elastic Half-Plane and the Infinite Plate with Two Finite Elastic Overlays in the Presence of Shear Interlayers. Proc. of the YSU A. Phys. Math. Sci. 49 (2015), 30-38. https://doi.org/10.46991/PYSU:A/2015.49.2.030

    Kerobyan A.V. Contact Problems for an Elastic Strip and the Infinite Plate with Two Finite Elastic Overlays in the Presence of Shear Interlayers. Proc. of the NAS of RA. Mechanics 67 (2014), 22-34 (in Russian). http://doi.org/10.33018/67.1.2

    Grigoryan E.Kh., Kerobyan A.V., Shahinyan S.S. The Contact Problem for the Infinite Plate with Two Finite Stringers One from which is Glued, Other is Ideal Conducted. Proc. of the NAS of RA. Mechanics 55 (2002), 14-23 (in Russian).

    Lubkin J.I., Lewis L.C. Adhesive Shear Flow for an Axially Loaded, Finite Stringer Bonded to an Infinite Sheet. Quarterly Journal of Mechanics and Applied Mathematics 23 (1970), 521-533. https://doi.org/10.1093/qjmam/23.4.521

    Kerobyan A.V., Sarkisyan V.S. The Solution of the Problem for an Anisotropic Half-plane on the Boundary of which Finite Length Stringer is Glued. Proc. of the Scientific Conference, Dedicated to the 60th Anniversary of the Pedagogical Institute of Gyumri. Gyumri, Vysshaya Shkola 1 (1994), 73-76 (in Russian).

    Grigoryan E.Kh. On Solution of Problem for an Elastic Infinite Plate, one the Surface of which Finite Length Stringer is Glued. Proc. of the NAS of RA. Mechanics 53 (2000), 11-16 (in Russian).

    Sarkisyan V.S., Kerobyan A.V. On the Solution of the Problem for Anisotropic Half-plane on the Edge of which a Nonlinear Deformable Stringer of Finite Length is Glued. Proc. of the NAS of RA. Mechanics 50 (1997), 17-26 (in Russian).

    Kerobyan A.V., Sahakyan K.P. Loads Transfer from Finite Number Finite Stringers to an Elastic Half-plane through Adhesive Shear Layers. Proc. of the NAS of RA. Mechanics 70 (2017), 39-56 (in Russian). https://doi.org/10.33018/70.3.4

    Kerobyan A.V. Transfer of Loads from a Finite Number of Elastic Overlays with Finite Lengths to an Elastic Strip through Adhesive Shear Layers. Proc. of the YSU A. Phys. Math. Sci. 53 (2019), 109-118. https://doi.org/10.46991/PYSU:A/2019.53.2.109

    Aghayan K.L. Some Contact Problems for an Elastic Infinite Plate Strengthened by Elastic Overlays. Proc. of the AS USSR. Mechanics of Solids 5 (1972), 34-45 (in Russian).

    Muki R., Sternberg E. On the Diffusion of Load from a Transverse Tension Bar into a Semi-infinite Elastic Sheet. Transactions of the ASME 4 (1968), 737-746. https://doi.org/10.1115/1.3601299

    Shilov G.E. Mathematical Analysis. Special Course. Moscow, State Publishing House of Physical and Mathematical Literature (1961), 442 (in Russian).

    Aleksandrov B.M., Mkhitaryan S.M. Contact Problems for Bodies with Thin Coatings and Interlayers. Moscow, Nauka (1983), 488 (in Russian).

    Grigolyuk E.I., Tolkachev V.M. Contact Problems of the Theory of Plates and Shells. Moscow, Mashinostroenie (1980), 416 (in Russian).

    Sarkisyan V.S. Contact Problems for Semi-planes and Strips with Elastic Overlays. Yerevan, YSU Publ. Press (1983), 260 (in Russian).

Informatics

  • Informatics

    OPERATIONAL ENVIRONMENT FOR DYNAMIC LOADING AND MANAGEMENT OF MODULAR APPLICATIONS IN EMBEDDED SYSTEMS

    Mkhitar V. Avetisyan, Kajik A. Avetisyan
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    Abstract

    Ensuring modularity under strict resource constraints is a critical challenge in the design of embedded systems. This paper presents "Part Only RTOS", an operational environment developed for devices based on 8-bit ATmega2560 microcontrollers. The core feature of the proposed system is the ability to dynamically load and execute application modules (in .bin format) from an SD card without requiring full hardware reprogramming. The developed three-tier architecture, comprising the Launcher, Bootloader, and Kernel, ensures reliable software validation via unique pattern matching and efficient memory utilisation. Experimental results demonstrate that the system kernel occupies less than $2.5~KB$ of Flash memory, and the loading time for a 10 KB module is approximately 132 ms. This solution enables the creation of flexible and high-performance modular embedded systems on low-power microcontroller platforms.

    References

    Williams E. Make: AVR Programming (1st Ed.). O'Reilly Media (2014).

    Bondarenko D.N. Vstraivaemye Mikrokontrolery AVR. Elec.ru (2018) (in Russian).

    Beningo J. Bootloader Design for Microcontrollers in Embedded Systems. Beningo Embedded Group (2015). https://www.beningo.com/

    Li Q., Yao C. Real-Time Concepts for Embedded Systems. CMP Books (2003). https://doi.org/10.1201/9781420025552

    Atienza D. Dynamic Memory Management for Embedded Systems. Springer (2015). https://doi.org/10.1007/978-3-319-10571-0

    Zlatanov N. Dynamic Memory Allocation and Fragmentation. ESC Santa Clara (2015).

    White E. Making Embedded Systems. O'Reilly Media (2011). https://doi.org/10.1002/9781119457503

    Ehrlich P., Radke S. Energy-aware Software Development for Embedded Systems in HW/SW Co-design. Workshop on SEES (2013). https://doi.org/10.1109/DDECS.2013.6549823

    Cha N. Petit FatFs Module Specification. [Online]. Available at: http://elm-chan.org/fsw/ff/00index_p.html

  • Informatics

    KNOWLEDGE-DISTILLED VARIATIONAL BAYESIAN FRAMEWORK FOR EFFICIENT LARGE-SCALE IMAGE DEHAZING

    Stepan V. Babayan
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    Abstract

    Real-time processing of large-scale image databases with state-of-the-art dehazing methods presents a significant computational challenge: methods that achieve superior generalization typically require substantial inference time, limiting their deployment in real-time applications. High-performing methods employ complex multi-scale processing and deep architectures, typically achievin less than 5 FPS on high-resolution images. Building on the preliminary multi-scale variational Bayesian framework [1, 2], which achieves strong synthetic-to-real generalization, this paper proposes knowledge distillation to transfer the generalization capabilities of high-performance models to a lightweight Vision Transformer-based student network. The student leverages patch-based processing and reduced architectural complexity to achieve over $150\times$ speedup, while maintaining competitive performance through a theoretically-grounded distillation framework integrated into the variational Bayesian objective. Additionally, the atmospheric scattering model is extended to estimate space-variant atmospheric light, improving performance on varying haze regions. Trained solely on synthetic Haze4K data, the proposed method stays competitive on synthetic-to-real generalization and downstream object detection (on the augmented KITTI dataset) tasks, while achieving superior inference speed for large-scale real-world applications.

    References

    Babayan S., Sáez-Maldonado F.J., et al. Bridging the Synthetic-to-real Gap in Single Image Dehazing. Digit. Signal Process (2026), 106097. https://doi.org/10.1016/j.dsp.2026.106097

    Babayan S., Sáez-Maldonado F.J., et al. Variational Bayesian Dehazing with Atmospheric Scattering Model for Synthetic-to-real Generalization. 2025 IEEE ICIPW (2025), 380-385.

    Wang Y., He B. CasDyF-Net: Image Dehazing via Cascaded Dynamic Filters. Proc. IEEE ICVISP (2024), 1-8. https://doi.org/10.1109/ICVISP64524.2024.10959384

    Wang T., Zang K. et al. Gridformer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions. IJCV 132 (2024), 4541-4563. https://doi.org/10.48550/arXiv.2305.17863

    Park B., Lee H., et al. Progressive Pruning of Light Dehaze Networks for Static Scenes. Applied Sciences 14 (2024), 10820. https://doi.org/10.3390/app142310820

    Zheng X. Research on Hardware Acceleration of Dehazing Network Based on Deep Learning. International Journal of Computer Science and Information Technology 5 (2025), 69–87. https://doi.org/10.62051/ijcsit.v5n1.07

    Fang L., Chen Y. , et al. Bayesian Knowledge Distillation: a Bayesian Perspective of Distillation with Uncertainty Quantification. Forty-first International Conference on Machine Learning (2024).

    Ming Y., Fu H., et al. Variational Bayesian Group-level Sparsification for Knowledge Distillation. IEEE Access 8 (2020), 126628-126636. https://doi.org/10.1109/ACCESS.2020.3008854

    McCartney E.J. Optics of the Atmosphere: Scattering by Molecules and Particles. New York (1976).

    He K., Sun J., Tang X. Single Image Haze Removal Using Dark Channel Prior. IEEE TPAMI 33 (2011), 2341-2353. https://doi.org/10.1109/CVPRW.2009.5206515

    Zhu Q., Mai J., Shao L. A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior. IEEE TIP 24 (2015), 3522-3533. http://dx.doi.org/10.1109/TIP.2015.2446191

    Fattal R. Dehazing Using Color-Lines. ACM Trans. Graph. 34 (2015), 1-14. https://doi.org/10.1145/2651362

    Zheng C., Ying W., Hu Q. Comparative Analysis of Dehazing Algorithms on Real-world Hazy Images. Scientific Reports 15 (2025), 10822. https://doi.org/10.1038/s41598-025-95510-z

    Cai B., Xu X., et al. DehazeNet: An End-to-end System for Single Image Haze Removal. IEEE TIP 25 (2016), 5187-5198. https://doi.org/10.48550/arXiv.1601.07661

    Li B., Peng X., et al. AOD-Net: All-in-one Dehazing Network. Proc. IEEE ICCV (2017), 4780-4788.

    Qin X., Wang Z., et al. FFA-Net: Feature Fusion Attention Network for Single Image Dehazing. Proc. AAAI Conf. on AI 34 (2020), 11908-11915.

    Cui Y., Ren W., et al. Revitalizing Convolutional Network for Image Restoration. IEEE TPAMI 46 (2024), 9423-9438. https://doi.org/10.1109/TPAMI.2024.3419007

    Cui Y., Ren W., et al. Image Restoration via Frequency Selection. IEEE TPAMI 46 (2024), 1093-1108. https://doi.org/10.1109/TPAMI.2023.3330416

    Chen Z., He Z., Lu Z.M. Dea-net: Single Image Dehazing Based on Detail-Enhanced Convolution and Content-guided Attention. IEEE TIP 33 (2024), 1002-1015. https://doi.org/10.1109/TIP.2024.3354108

    Hong M., Xie Y., et al. Distilling Image Dehazing with Heterogeneous Task Imitation. Proc. IEEE/CVF CVPR (2020), 3462-3471.

    Wu H., Liu J., et al. Knowledge Transfer Dehazing Network for Non-homogeneous Dehazing. Proc. IEEE/CVF CVPRW (2020), 478-479.

    Tran L.A., Park D.C. Lightweight Image Dehazing Networks Based on Soft Knowledge Distillation. The Visual Computer 41 (2025), 4047-4066. https://doi.org/10.1007/s00371-024-03645-3

    Chen Z., Bi X., et al. Omni-scale Feature Learning for Lightweight Image Dehazing. Applied Intelligence 54 (2024), 10039-10054. https://doi.org/10.1007/s10489-024-05721-6

    Lu H., Li Y., et al. Single Image Dehazing through Improved Atmospheric Light Estimation. Multimed. Tools Appl. 75 (2016), 17081-17096. https://doi.org/10.48550/arXiv.1510.01018

    He Y., Li C., Li X. Remote Sensing Image Dehazing Using Heterogeneous Atmospheric Light Prior. IEEE Access 11 (2023), 18805-18820. https://doi.org/10.1109/ACCESS.2023.3247967

    Kulkarni A., Murala S. Aerial Image Dehazing with Attentive Deformable Transformers. Proc. IEEE/CVF WACV (2023), 6305-6314.

    Koonce B. Mobilenetv3. Convolutional Neural Networks with Swift for TensorFlow: Image Recognition and Dataset Categorization. Springer (2021), 125-144.

    Mehta S., Rastegari M. Mobilevit: Light-Weight, General-Purpose, and Mobile-Friendly Vision Transformer (2021). https://doi.org/10.48550/arXiv.2110.02178

    Hong M., Liu J., et al. Uncertainty-Driven Dehazing Network. Proc. AAAI Conf. on AI 36 (2022), 906-913. https://doi.org/10.1609/aaai.v36i1.19973

    Wang B., Ning Q., et al. Uncertainty Modeling of the Transmission Map for Single Image Dehazing. IEEE TCSVT https://doi.org/10.1109/TCSVT.2024.3412093

    Im E.W., Shin J., et al. Deep Variational Bayesian Modeling of Haze Degradation Process. Proc. ACM CIKM (2023), 895-904. https://doi.org/10.1145/3583780.3614838

    Jia X., De Brabandere B., et al. Dynamic Filter Networks. Proc. NeurIP (2016), 667-675.

    Chen D., He M., et al. Gated Context Aggregation Network for Image Dehazing and Deraining. Proc. IEEE WACV (2019), 1375-1383. https://doi.org/10.48550/arXiv.1811.08747

    Liu Y., Zhu L. et al. From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data. Proc. 29th ACM Int. Conf. on Multimedia (2021), 50-58. https://doi.org/10.1145/3474085.3475331

    Ancuti C.O., Ancuti C., et al. O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-free Outdoor Images. Proc. IEEE CVPRW (2018), 754-762. https://doi.org/10.48550/arXiv.1804.05101

    Ancuti C.O., Ancuti C., et al. I-HAZE: A Dehazing Benchmark with Real Hazy and Haze-free Indoor Images. Proc. ACIVS (2018), 620--631.

    Ancuti C.O., Ancuti C., Timofte R. Nh-haze: An Image Dehazing Benchmark with Non-homogeneous Hazy and Haze-free Images. Proc. IEEE/CVF CVPRW (2020), 444-445. https://doi.org/10.1109/CVPRW50498.2020.00230

    Geiger A., Lenz P., et al. Vision Meets Robotics: The Kitti Dataset. IJRR 32 (2013), 1231-1237. https://doi.org/10.1177/0278364913491297

    Sohan M., Sai Ram T., et al. A Review on Yolov8 and Its Advancements. Proc. ICDICI (2024), 529-545. https://doi.org/10.1007/978-981-99-7962-2_39

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