Image and Signal Denoising Methods
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This paper proposes a meta-filtration framework for denoising images corrupted by mixed noise (Gaussian, salt&pepper, speckle) that uses a manually defined filter set and automatically selects combinations that improve image quality metrics (PSNR or MS-SSIM).
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This paper proposes a meta-filtration framework for denoising images corrupted by mixed noise (Gaussian, salt&pepper, speckle). Instead of fixed pipelines or AI-based methods, it uses a manually defined filter set and automatically selects combinations that improve image quality metrics (PSNR or MS-SSIM). The approach was tested on images of different sizes and mixed noise levels. Results show PSNR improvements of 7- 11dB, with MS-SSIM confirming preservation of fine details. An embedded implementation on a Zynq-7000 SoC achieved similar quality to MATLAB (within 0.1-0.8dB) and significantly reduced runtime, demonstrating practical efficiency on resourceconstrained hardware.
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@article{Kanty2026Meta,
title = {Meta-filtration: Adaptive selection of multiple filter cascades for images with quality analysis},
author = {Dominika Kanty and Jędrzej Sikora},
journal = {International Journal of Electronics and Telecommunications},
year = {2026},
doi = {10.24425/ijet.2026.1723},
url = {https://doi.org/10.24425/ijet.2026.1723}
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