Optimization of Observations for a Limited Flow of Random Processes

Boldyrikhin, Nikolay; Safaryan, Olga; Svizhenko, Alexey

doi:10.1109/rsemw52378.2021.9494057

Cited by 1 publication

(4 citation statements)

References 7 publications

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“…The operation of the Kalman algorithm consists of two stages, which alternate with each other: the prediction stage (extrapolation) and the correction stage (updating) [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The prediction stage consists of obtaining the current state vector based on the system model and the effects on it and obtaining the covariance error matrix, which describes the accuracy of the state estimation obtained by the filter.…”

Section: Methodsmentioning

confidence: 99%

“…Often the source of such distortions is the noise that occurs in the observation channel. In such problems, the Kalman filtering algorithm [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] has shown itself well.…”

Section: Introductionmentioning

confidence: 99%

“…The Kalman filter is a data processing algorithm that allows estimating the state of a dynamic system from incomplete or noisy measurements [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…The Kalman filter has been successfully used in various scientific and applied fields, such as meteorology, estimation of object motion parameters, process control systems [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], and image processing [10][11][12][13][14][15][16][17][18]. It seems relevant to further study the possibilities of Kalman filtering in image processing.…”

Section: Introductionmentioning

confidence: 99%

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Filtering grayscale images using the Kalman filter

et al. 2022

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This article aims to explore the possibility of using the Kalman filter to filter images. The relevance of the study lies in the fact that at present the tasks of image processing have become of great importance in many areas, such as industry, science, medicine, space industry and agriculture. Methods for improving image quality are of great applied and scientific interest for the agricultural sector, since machine vision methods are now widely used in assessing the condition of agricultural plants, soil condition, sorting of agricultural products, controlling unmanned agricultural machines, etc. The purpose of this work is to develop an algorithm and software for filtering grayscale images. The article consists of four parts: Introduction, Materials and methods, Results, Conclusions. The first part describes the relevance of the topic, discusses the reasons for obtaining noisy images. The second part describes the Kalman filter algorithm as applied to image filtering problems. In the third part, the results of the software implementation of the developed algorithm are considered, which make it possible to evaluate the quality of image filtering. In the fourth part conclusions are drawn and summed up. The main results of the work are the algorithmic implementation of noise removal from halftone images grayscale images using a software tool developed as part of these studies.

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