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A BEMD-Based Quaternion Filtering Approach Sharp-to-Soft Kernel CT Image Conversion — arXiv:2610.07071

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07071
License (as archived): CC0-1.0
Archived: 2026-10-07T13:07:00.302Z
SHA-256 of the archived text: 032b3e8cb5fff058be284fe2315c70d4e90ad4be742dc39347187d617e464a44
Stable URL of this page: https://metraipolis.com/documento/dc_152ffdf8ffe0

How to cite

A BEMD-Based Quaternion Filtering Approach Sharp-to-Soft Kernel CT Image Conversion — arXiv:2610.07071 — arXiv (en), https://arxiv.org/abs/2610.07071 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_152ffdf8ffe0 (Metraipolis archive dc_152ffdf8ffe0, sha256 032b3e8cb5fff058be284fe2315c70d4e90ad4be742dc39347187d617e464a44)

BibTeX

@misc{dc152ffdf8ffe0,
  title = {A BEMD-Based Quaternion Filtering Approach Sharp-to-Soft Kernel CT Image Conversion — arXiv:2610.07071},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07071},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_152ffdf8ffe0; sha256 032b3e8cb5fff058be284fe2315c70d4e90ad4be742dc39347187d617e464a44},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07071. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The quality of computed tomography (CT) images is significantly affected by the selection of reconstruction kernels: sharp kernels improve spatial resolution but increase noise, whereas soft kernels diminish noise at the expense of edge clarity. This study presents an innovative enhancement framework utilising Bidimensional Empirical Mode Decomposition in conjunction with Quaternion Bilateral Filtering (BEMD--QBF) to convert sharp-kernel CT images into representations resembling soft-kernels, while maintaining critical anatomical structures. The technique disaggregates each image into intrinsic mode functions via BEMD and analyzes them inside a cohesive quaternion framework to attain efficient noise reduction and structural integrity. The proposed methodology is evaluated using several reconstruction kernels (B50, B46, B41, B36, B35, B31) and compared with recognised filtering strategies, including Non-Local Means, Anisotropic Diffusion, Bilateral Filtering, and Quaternion Bilateral Filtering. Quantitative evaluations of the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) indicate that BEMD-QBF consistently attains superior structural fidelity and competitive noise reduction across all evaluated kernels. The results underscore the efficacy of the proposed strategy as a viable approach to enhancing post-reconstruction CT images, yielding superior image quality without requiring access to raw projection data.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.07071 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T12:37:32.854Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).