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Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion — arXiv:2608.29227

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29227
License (as archived): CC0-1.0
Archived: 2026-10-17T09:53:45.137Z
SHA-256 of the archived text: f2ee896abd004d43c431b3929f2276fd21d9c274a5d8781d6aba71912724758c
Stable URL of this page: https://metraipolis.com/documento/dc_101ef4c43352

How to cite

Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion — arXiv:2608.29227 — arXiv (en), https://arxiv.org/abs/2608.29227 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_101ef4c43352 (Metraipolis archive dc_101ef4c43352, sha256 f2ee896abd004d43c431b3929f2276fd21d9c274a5d8781d6aba71912724758c)

BibTeX

@misc{dc101ef4c43352,
  title = {Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion — arXiv:2608.29227},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29227},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_101ef4c43352; sha256 f2ee896abd004d43c431b3929f2276fd21d9c274a5d8781d6aba71912724758c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29227. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update scheme we formulate a novel energy functional using a soft-histogram representation of image pixel neighborhoods obtained from the channel encoding. The resulting Euler-Lagrange equation yields a non-linear robust diffusion scheme with additional weighting terms stemming from the channel representation which steer the diffusion process. We apply this novel energy formulation to image reconstruction problems, showing good performance in the presence of mixtures of Gaussian and impulse-like noise, e.g. missing data. In denoising experiments of common scalar-valued images our approach performs competitive compared to other diffusion schemes as well as state-of-the-art denoising methods for the considered noise types.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-01 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.29227 — 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 2026-10-17T09:28:48.007Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).