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DAMRmF for Salt and Pepper Noise Removal

version 1.0 (214 KB) by Samet Memis
Code of the paper titled "Different Adaptive Modified Riesz Mean Filter For High-Density Salt-and-Pepper Noise Removal in Grayscale Images"

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Updated 10 Apr 2021

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Different Adaptive Modified Riesz Mean Filter For High-Density Salt-and-Pepper Noise Removal in Grayscale Images

Citation:

S. Memiş and U. Erkan, 2021. Different Adaptive Modified Riesz Mean Filter For High-Density Salt-and-Pepper Noise Removal in Grayscale Images, European Journal of Science and Technology, (23), 359-367. doi: https://doi.org/10.31590/ejosat.873312

Abstract:

This paper proposes a new filter, Different Adaptive Modified Riesz Mean Filter (DAMRmF), for high-density salt-and-pepper noise (SPN) removal. DAMRmF operationalizes a pixel weight function and adaptivity condition of Adaptive Median Filter (AMF). In the simulation, the proposed filter is compared with Adaptive Frequency Median Filter (AFMF), Three-Values-Weighted Method (TVWM), Unbiased Weighted Mean Filter (UWMF), Different Applied Median Filter (DAMF), Adaptive Weighted Mean Filter (AWMF), Adaptive Cesáro Mean Filter (ACmF), Adaptive Riesz Mean Filter (ARmF), and Improved Adaptive Weighted Mean Filter (IAWMF) for 20 traditional test images with noise levels from 60% to 90%. The results show that DAMRmF outperforms the state-of-the-art filters in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) values. Moreover, DAMRmF also performs better than the state-of-the-art filters concerning mean PSNR and SSIM results. We finally discuss DAMRmF for further research.

Cite As

S. Memiş and U. Erkan, 2021. Different Adaptive Modified Riesz Mean Filter For High-Density Salt-and-Pepper Noise Removal in Grayscale Images, European Journal of Science and Technology, (23), 359-367. doi: https://doi.org/10.31590/ejosat.873312

MATLAB Release Compatibility
Created with R2021a
Compatible with any release
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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.