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New method for detecting and removing random-valued impulse noise from images
P.A. Lyakhov 1,2, A.R. Orazaev 2

1 North-Caucasus Federal University, 355017, Stavropol, Russia, Pushkin street, 1;
2 North-Caucasus Center for Mathematical Research, 355017, Stavropol, Russia, Pushkin street, 1

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DOI: 10.18287/2412-6179-CO-1145

Pages: 262-271.

Full text of article: Russian language.

Abstract:
The paper proposes a method for detecting and removing impulse noise in images, which determines the similarity between pixels by distance and the difference in brightness values in the local detector window. An impulse noise model is considered, where distorted pixels take random values and also randomly appear in the image. Pixels identified as pulses are recovered with an adaptive median filter. The impulses are determined in the detector window, whose size is calculated in the Euclidean metric and increases with increasing noise intensity in the image. In the experimental part, we discuss visual differences between familiar methods and the one proposed herein on three images for three different impulse noise intensities. In the approximation of image fragments, it is seen that the proposed method copes with the task in the best way, which is also confirmed by numerical estimates of the quality of image reconstruction from impulse noise based on the peak signal-to-noise ratio and the structural similarity index. The proposed method can be used when solving problems of cleaning images under conditions of distorting impulses and for eliminating distortions caused by adverse weather effects, such as raindrops and snow.

Keywords:
image processing, impulse noise, median filter, adaptive filter.

Citation:
Lyakhov PA, Orazaev AR. New method for detecting and removing random-valued impulse noise from images. Computer Optics 2023; 47(2): 262-271. DOI: 10.18287/2412-6179-CO-1145.

Acknowledgements:
The authors thank the North-Caucasus Federal University for the award of funding in the contest of competitive projects of scientific groups and individual scientists of North-Caucasus Federal University. The research in section 1 and section 2 was supported by the Russian Science Foundation (Project No. 21-71-00017). The research in section 3 was supported by the North-Caucasus Center for Mathematical Research under agreement with the Ministry of Science and Higher Education of the Russian Federation (Agreement No. 075-02-2022-892).

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