Meta-filtration: Adaptive selection of multiple filter cascades for images with quality analysis

Authors

  • Dominika Kanty Department of Electronic and Photonic Metrology, Faculty of Electronics, Photonics and Microsystems, Wroclaw University of Science and Technology https://orcid.org/0009-0009-0035-4371
  • Jędrzej Sikora Department of Electronic and Photonic Metrology, Faculty of Electronics, Photonics and Microsystems, Wroclaw University of Science and Technology

Abstract

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 resource-constrained hardware.

Additional Files

Published

2026-07-17

Issue

Vol. 72 No. 3 (2026)

Section

VHDL, Hardware Intelligence

License

Copyright (c) 2026 International Journal of Electronics and Telecommunication

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