Szczegóły publikacji
Opis bibliograficzny
ViT-SpSH: a hybrid transformer-spectral head architecture for chromatic aberration detection and localization / Jarosław Bernacki, Rafał SCHERER // W: Computational Science – ICCS 2026 workshops : 26th International Conference : Hamburg, Germany, June 29–July 1, 2026 : proceedings , Pt. 2 / eds. Maciej Paszynski, Amanda S. Barnard, Yongjie Jessica Zhang. — Cham : Springer, cop. 2026. — ( Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 16787 ). — ISBN: 978-3-032-29908-6; e-ISBN: 978-3-032-29909-3. — S. 413–427. — Bibliogr., Abstr. — Publikacja dostępna online od: 2026-06-28. — R. Scherer - dod. afiliacje: Częstochowa University of Technology ; Center of Excellence in Artificial Intelligence, AGH University of Krakow
Autorzy (2)
- Bernacki Jarosław
- AGHScherer Rafał
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 168915 |
|---|---|
| Data dodania do BaDAP | 2026-08-31 |
| DOI | 10.1007/978-3-032-29909-3_30 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2026 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Chromatic aberration remains a common optical artifact in digital photography, manifesting as color fringing along high-contrast edges due to wavelength-dependent lens refraction. This paper proposes a hybrid vision transformer-based architecture for the accurate detection and localization of chromatic aberration in single RGB images. The method employs a pretrained ViT encoder to extract global contextual patch embeddings capturing long-range spatial dependencies. In parallel, chromatic residual maps are computed from inter-channel differences to explicitly highlight spectral misalignments. These residuals are fused early with ViT embeddings, followed by cross-attention refinement and convolutional upsampling in a dedicated spectral segmentation head, yielding a high-resolution probability map of aberration regions. Experiments on a diverse dataset of real-world photographs demonstrate that the proposed hybrid approach significantly outperforms classical convolutional baselines (classic CNN, U-Net, FCN-VGG) in classification accuracy, offering a robust tool for automated optical quality assessment.
