Adaptive weight-modified Riesz mean filter for high-density salt-and-pepper noise removal

Abstract This paper introduces a novel filter, the adaptive weight-modified Riesz mean filter (AWMRmF), designed for the effective removal of high-density salt-and-pepper noise. AWMRmF integrates a pixel weight function and adaptivity condition inspired by the different adaptive modified Riesz mean filter. In this study, the performance of AWMRmF was evaluated against established filters such as adaptive frequency median filter, adaptive weighted mean filter, adaptive Ces´aro mean filter, adaptive Riesz mean filter, and improved adaptive weighted mean filter. The assessment was conducted on 26 typical test images, varying noise levels from 60 to 95%. The findings indicate that, in terms of both peak signal-to-noise ratio and structural similarity metrics, AWMRmF outperformed other state-of-the-art filters. Furthermore, AWMRmF demonstrated superior performance in mean PSNR and SSIM results as well.

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Publication Details

Journal
Journal of Electrical Systems and Information Technology
Published
2025-09-15
DOI
https://doi.org/10.1186/s43067-025-00266-1
Citations
1
Primary Topic
Advanced Adaptive Filtering Techniques
Type
article
Field-Weighted Citation Impact
1.33
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article

Adaptive weight-modified Riesz mean filter for high-density salt-and-pepper noise removal

M. Islam
1 citations
Journal of Electrical Systems and Information Technology
Advanced Adaptive Filtering Techniques
1.33
article

Adaptive weight-modified Riesz mean filter for high-density salt-and-pepper noise removal

M. Islam
article en
1 citations

Abstract

Abstract This paper introduces a novel filter, the adaptive weight-modified Riesz mean filter (AWMRmF), designed for the effective removal of high-density salt-and-pepper noise. AWMRmF integrates a pixel weight function and adaptivity condition inspired by the different adaptive modified Riesz mean filter. In this study, the performance of AWMRmF was evaluated against established filters such as adaptive frequency median filter, adaptive weighted mean filter, adaptive Ces´aro mean filter, adaptive Riesz mean filter, and improved adaptive weighted mean filter. The assessment was conducted on 26 typical test images, varying noise levels from 60 to 95%. The findings indicate that, in terms of both peak signal-to-noise ratio and structural similarity metrics, AWMRmF outperformed other state-of-the-art filters. Furthermore, AWMRmF demonstrated superior performance in mean PSNR and SSIM results as well.

Journal of Electrical Systems and Information TechnologyVol. 12(1)
Bangladesh University of Engineering and Technology (BD)
Openalex Percentile: Top 18%
Advanced Adaptive Filtering Techniques
1.33
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Adaptive weight-modified Riesz mean filter for high-density salt-and-pepper noise removal — M. Islam · Journal of Electrical Systems and Information Technology (2025) | TGRS Research Map | TGRS