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A Gradient-Regularized Deep Learning Method for Restoring Image in Myopic Vision — arXiv:2610.19218

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.19218
License (as archived): CC0-1.0
Archived: 2026-10-15T21:55:45.170Z
SHA-256 of the archived text: 4f59bac3c276e3fa8cff1c9d866ea01fad8003264b0a5bdff5c5fd70f31de6e1
Stable URL of this page: https://metraipolis.com/documento/dc_9b5f97fed65b

How to cite

A Gradient-Regularized Deep Learning Method for Restoring Image in Myopic Vision — arXiv:2610.19218 — arXiv (en), https://arxiv.org/abs/2610.19218 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_9b5f97fed65b (Metraipolis archive dc_9b5f97fed65b, sha256 4f59bac3c276e3fa8cff1c9d866ea01fad8003264b0a5bdff5c5fd70f31de6e1)

BibTeX

@misc{dc9b5f97fed65b,
  title = {A Gradient-Regularized Deep Learning Method for Restoring Image in Myopic Vision — arXiv:2610.19218},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.19218},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_9b5f97fed65b; sha256 4f59bac3c276e3fa8cff1c9d866ea01fad8003264b0a5bdff5c5fd70f31de6e1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.19218. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Myopic vision degrades the quality of human vision due to spherical eye defocus aberrations that impair the perception of fine spatial details. To address this limitation, this work proposes a deep learning-based approach to computationally design transformed images that allow individuals with myopia in fixed distance to perceive scenes with enhanced clarity. The method trains a neural network to counteract defocus aberrations modeled using the second-order Zernike polynomial $Z_2^0$. The approach employs a U-Net architecture trained with a loss function combining reconstruction error minimization with spatial difference structural preservation through $\ell_1$ regularization. Training is carried out exclusively on the KITTI dataset, while testing is performed on DIV2K and ImageNet datasets, demonstrating generalization across diverse image distributions. Under myopic conditions modeled by $Z_2^0$, baseline performance shows visual degradation with PSNR values ranging from 19.47 to 27.42 dB across datasets and severity levels. The proposed gradient-regularized method achieves consistent improvements, delivering PSNR gains ranging from 1.48 to 7.34 dB and significantly lower perceptual compared to classical baselines.

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/2610.19218 — 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 2026-10-15T21:38:29.447Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).