Geodesic
Comparative analysis of filtration algorithms for images of architectural plans
Problemy fiziki, matematiki i tehniki, no. 3 (2024), pp. 86-91.

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The use of smoothing filters for pre-processing images of architectural plans is considered. A comparative analysis of Gaussian and Roudin – Osher – Fatemi (ROF) filters based on the Chambolle model is carried out. The software modules are implemented in the Python programming language using OpenCV. The results showed that for pre-processing not very noisy images it is better to use a Gaussian filter, and for images with a high noise level it is better to use the ROF filter, which prevents the loss of special corner points.
Keywords: preprocessing, filtering, Gaussian blur, Gaussian filter, Rudin – Osher – Fatemi filter, Chambolle model, Python, OpenCV.
Mots-clés : noise 
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N. A. Aksionova. Comparative analysis of filtration algorithms for images of architectural plans. Problemy fiziki, matematiki i tehniki, no. 3 (2024), pp. 86-91. http://geodesic.mathdoc.fr/item/PFMT_2024_3_a14/

[1] C. Wang, L. Tian, “An improved adaptive median filter for Image denoising”, ICCEE, 53:2 (2012), 64

[2] C. Abe, “Iterative Edge-Preserving adaptive Wiener filter for image denoising”, ICCEE, 4:4 (2012), 503-506

[3] L.I. Rudin, S. Osher, E. Fatemi, “Nonlinear total variation based noise removal algorithms”, Physica D, 60 (1992), 259-268 | DOI | MR | Zbl

[4] A. Chambolle et al., “An introduction to total variation for image analysis”, Theoretical foundations and numerical methods for sparse recovery, 9 (2009), 263-340 | MR

[5] T.F. Chan, J. Shen, Image Processing and Analysis: Variational, PDE, wavelet, and Stochastic Methods, SIAM, 2005 | MR | Zbl

[6] Rudin-Osher-Fatemi total variation denoising using split Bregman, IPOL, 2012 (Date of access: 26.06.2024) http://www.ipol.im/pub/art/2012/g-tvd/

[7] J.J. Hwang, K.H. Rhee, “Gaussian Forensic Detection using Blur Quantity of Forgery Image”, 2019 International Conference on Green and Human Information Technology (ICGHIT), IEEE, 2019, 86–88 | DOI

[8] M. Moradifar, A. Shahbahrami, “Performance Improvement of Gaussian Filter using SIMD Technology”, 2020 International Conference on Machine Vision and Image Processing (MVIP), 2020, 1-6

[9] J. Achin, X.N. Truong, M. Morari, “Learning and control using Gaussian processes”, ACM/IEEE 9th International Conference on CyberPhysical Systems (ICCPS), IEEE, 2018, 140-149

[10] G. Cao G., E.M.K. Lai, F. Alam, “Gaussian process model predictive control of unknown non-linear systems”, IET Control Theory Applications, 11:5 (2017), 703-713 | DOI | MR

[11] M.P. Deisenroth, R.D. Turner, M.F. Huber et al., “Robust filtering and smoothing with Gaussian processes”, IEEE Transactions on Automatic Control, 57:7 (2012), 1865-1871 | DOI | MR | Zbl

[12] N.A. Aksenova, A.I. Kucherov, “Razrabotka SDK dlya mobilnogo prilozheniya s primeneniem tekhnologii dopolnennoi realnosti”, Izvestiya Gomelskogo gosudarstvennogo universiteta imeni F. Skoriny, 2021, no. 126 (3), 81-84

[13] O.M. Demidenko, N.A. Aksionova, “Development of a Machine Vision System for Image Recognition of Design Estimates”, Nonlinear Phenomena in Complex Systems, 25:2 (2022), 159-167 | DOI | MR

[14] N.A. Aksionova, D.S. Sych, A.V. Varuyeu, “Method of construction of three-dimensional structures based on key corner points”, Izvestia of F. Skorina Gomel State University, 2021, no. 6 (141), 69-75

[15] O.M. Demidenko, N.A. Aksionova, A.V. Varuyeu, “3D-modeling of Augmented Reality objects using Shi-Tomasi corner detection algorithms”, J. Phys. CS, 2091 (2021), 012058 | DOI

[16] N.A. Aksionova, O.M. Demidenko, A.V. Varuyeu, D.S. Sych, “Modification of the Canny Edge Detector for Recognition of Image Markers of Architectural Plans”, 2023 7th International Conference on Information, Control, and Communication Technologies (ICCT) (Astrakhan, Russian Federation, 2023) | DOI