Використання клітинних автоматів для вирішення задач фільтрації шумів та виявлення контурів зображень
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Date
2019
Authors
Жежерун, Олександр
Калітовський, Богдан
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Abstract
У статті проведено огляд застосування клітинних автоматів для обробки та аналізу зображень. Наведено опис фільтрів, що можуть видаляти імпульсний шум із пошкоджених шумом зображень, і методів визначення контурів на зображеннях,реалізованих на основі клітинних автоматів. Продуктивність цих підходів було порівняно із традиційними методами: медіанним фільтром (для шумозаглушення) та перехресним оператором Робертса, оператором Собеля–Фельдмана, оператором Лапласа (для визначення контурів). Це порівняння засвідчує, що наведені методи на основі клітинних автоматів є дуже перспективними для фільтрації імпульсних шумів і виявлення контурів зображень.
Cellular Automata (CA) are the most common and simple models of parallel computations. CA can be successfully applied in image processing, where we consider images as a system of simple components (pixels), and the behaviour of each component is obtained and reformed according to the behaviour of their neighbours and their previous behaviour. The constructive components of these systems can perform reliable and complex tasks by interacting with each other. Precisely by setting certain rules of the behaviour of the components, the cellular automata achieved significant results in such areas of image processing as noise filtering, smoothing, edge detection, restoring and extracting the features of images, figures and texts recognition, image compression. However, up to these days corresponding researches remain being used only in order to solve specified tasks, such as image processing of minefields, the processing of X-ray imagesin medicine, or the analysis of satellite imagery. This paper reviews the application of CA for image analysis and processing. It demonstrates an image noise filter based on CA, which can remove impulse noise from a noise-corrupted image and compares it with the median filter. Meanwhile, the edge detection appears to be one of the most crucial tasks in image processing (especially for biological and medical images processing). So CA based edge detection has potential benefits over known traditional approaches since it is computationally efficient, and can be tuned for specific applications by appropriate selection or learning of rules. Several CA based edge detection methods are implemented and tested to enable an initial comparison between existing traditional methods (the Roberts cross operator, Sobel-Feldman operator, Laplace operator). This comparisons show that the provided CA-based methods are very perspective for impulse noise filtering and image edge detection.
Cellular Automata (CA) are the most common and simple models of parallel computations. CA can be successfully applied in image processing, where we consider images as a system of simple components (pixels), and the behaviour of each component is obtained and reformed according to the behaviour of their neighbours and their previous behaviour. The constructive components of these systems can perform reliable and complex tasks by interacting with each other. Precisely by setting certain rules of the behaviour of the components, the cellular automata achieved significant results in such areas of image processing as noise filtering, smoothing, edge detection, restoring and extracting the features of images, figures and texts recognition, image compression. However, up to these days corresponding researches remain being used only in order to solve specified tasks, such as image processing of minefields, the processing of X-ray imagesin medicine, or the analysis of satellite imagery. This paper reviews the application of CA for image analysis and processing. It demonstrates an image noise filter based on CA, which can remove impulse noise from a noise-corrupted image and compares it with the median filter. Meanwhile, the edge detection appears to be one of the most crucial tasks in image processing (especially for biological and medical images processing). So CA based edge detection has potential benefits over known traditional approaches since it is computationally efficient, and can be tuned for specific applications by appropriate selection or learning of rules. Several CA based edge detection methods are implemented and tested to enable an initial comparison between existing traditional methods (the Roberts cross operator, Sobel-Feldman operator, Laplace operator). This comparisons show that the provided CA-based methods are very perspective for impulse noise filtering and image edge detection.
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Keywords
клітинні автомати, обробка зображень, шумозаглушення, визначення контурів, лінійне правило, стаття, cellular automata, image processing, noise filtering, edge detection, linear rule, article
Citation
Жежерун О. П. Використання клітинних автоматів для вирішення задач фільтрації шумів та виявлення контурів зображень / Жежерун О. П., Калітовський Б. В. // Наукові записки НаУКМА. Комп'ютерні науки. - 2019. - Т. 2. - С. 66-72.