Analysis and improvement of methods and means for eliminating distortions in image and video signals
Abstract
The main purpose of this article was to study existing image processing methods, binarization and
noise reduction, and to develop an improved adaptive thresholding algorithm. The analysis of
methods and tools for eliminating distortions in image and video signals is an urgent task in
numerous fields, including medicine, computer vision, and document science. The article discusses
in detail the existing binarization and noise reduction methods, highlighting their advantages and
limitations. However, the main achievement is the development and implementation of an
improved adaptive thresholding algorithm. This algorithm considers the specific features of the
image and automatically adapts the binarization threshold for better processing quality. It is a
significant contribution to the field of image processing and can be used in various fields,
including medical diagnostics and visual object detection in images.
References
Zhurba A., Gasik M. Binarization methods and investigation of their influence on the fractal dimension of functional coatings. Modern Problems of Metalurgy. 2020. No. 23. P. 30–42. URL: https://doi.org/10.34185/1991-7848.2020.01.04 (date of access: 10.10.2023).
Vlăsceanu G. V., Tarbă N. Harnessing Neural Networks for Enhancing Image Binarization Through Threshold Combination. BRAIN. Broad Research in Artificial Intelligence and Neuroscience. 2023. Vol. 14, no. 2. P. 59–75. URL: https://doi.org/10.18662/brain/14.2/444 (date of access: 10.10.2023).
Polyakova M. V., Nesteryuk A. G. IMPROVEMENT OF THE COLOR TEXT IMAGE BINARIZATION METHOD USING THE MINIMUM-DISTANCE CLASSIFIER. Applied Aspects of Information Technology. 2021. Vol. 4, no. 1. P. 57–70. URL: https://doi.org/10.15276/aait.01.2021.5 (date of access: 10.10.2023).
Binary Ghost Imaging Based on the Fuzzy Integral Method / X. Yang et al. Applied Sciences. 2021. Vol. 11, no. 13. P. 6162. URL: https://doi.org/10.3390/app11136162 (date of access: 10.10.2023).
Masaya Takagi, Misaki Kinoshita-Ise, Masahiro Fukuyama, Saori Nishikawa, Mami Miyoshi, Takaki Sugimoto, Masako Yamazaki, Masashi Ogo, Manabu Ohyama, Invention of automated numerical algorithm adopting binarization for the evaluation of scalp hair coverage: An image analysis providing a substitute for phototrichogram and global photography assessment for hair diseases, Journal of Dermatological Science, 2023, ISSN 0923-1811, https://doi.org/10.1016/j.jdermsci.2023.09.003 .
Теорія і Практика Обробки Сигналів у Малохвильовій(Wavelet)Області. / Наконечний А. Й., Лагун І. І., Верес З. Є., Наконечний Р. А., Федак В. І., (2020).
Adhari F. M., Abidin T. F., Ferdhiana R. License Plate Character Recognition using Convolutional Neural Network. Journal of Information Systems Engineering and Business Intelligence. 2022. Vol. 8, no. 1. P. 51–60. URL: https://doi.org/10.20473/jisebi.8.1.51-60 (date of access: 10.10.2023).
A Combined Approach for the Binarization of Historical Tibetan Document Images / Y. Han et al. International Journal of Pattern Recognition and Artificial Intelligence. 2019. Vol. 33, no. 14.P.1954038.URL: https://doi.org/10.1142/s0218001419540387 (date of access: 10.10.2023).
Vahid Rezanezhad, Konstantin Baierer, and Clemens Neudecker. 2023. A hybrid CNN-Transformer model for Historical Document Image Binarization. In Proceedings of the 7th International Workshop on Historical Document Imaging and Processing (HIP '23).
Copyright (c) 2023 Орест Денисюк, Ростислав Наконечний (Автор)
This work is licensed under a Creative Commons Attribution 4.0 International License.
