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Efficient Image Denoising on Smartphone GPUs: Mobile AI 2026 Challenge Report — arXiv:2611.00974

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00974
License (as archived): CC0-1.0
Archived: 2026-11-03T11:05:39.390Z
SHA-256 of the archived text: fac1b857f016b2acc29563dc18c46caebe21197fad84325362c5a9ee8bd34816
Stable URL of this page: https://metraipolis.com/documento/dc_0240acfd49ea

How to cite

Efficient Image Denoising on Smartphone GPUs: Mobile AI 2026 Challenge Report — arXiv:2611.00974 — arXiv (en), https://arxiv.org/abs/2611.00974 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0240acfd49ea (Metraipolis archive dc_0240acfd49ea, sha256 fac1b857f016b2acc29563dc18c46caebe21197fad84325362c5a9ee8bd34816)

BibTeX

@misc{dc0240acfd49ea,
  title = {Efficient Image Denoising on Smartphone GPUs: Mobile AI 2026 Challenge Report — arXiv:2611.00974},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00974},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0240acfd49ea; sha256 fac1b857f016b2acc29563dc18c46caebe21197fad84325362c5a9ee8bd34816},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00974. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Image denoising is a fundamental problem in mobile photography. Despite a large number of solutions proposed for this task, the majority of them are trained on synthetically produced noisy images, and are additionally too computationally heavy for processing full-resolution images on mobile devices. This creates a need for efficient deep learning-based models that are not only performant but can also be deployed on constrained mobile hardware. In this Mobile AI challenge, we address this problem and propose the participants to design fast solutions that can denoise Full HD resolution photos while running on popular smartphone GPUs from Qualcomm and Arm. For this, the participants were provided with a large-scale dataset consisting of noisy-clean image pairs captured in the wild. The runtime of all models was evaluated on the Snapdragon 8 Elite Gen 5 and Dimensity 9500 chipsets with the Adreno and Mali GPUs, respectively. Top solutions proposed in this challenge are able to perform high-quality image denoising under 100 milliseconds on both platforms while reaching a PSNR score of over 37 dB. A comprehensive description of the models developed in the challenge is provided in this paper.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.00974 — 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 2026-11-03T10:59:45.985Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).