Fiche publication
Date publication
janvier 2026
Journal
Sensors (Basel, Switzerland)
Auteurs
Membres identifiés du Cancéropôle Est :
Pr GOUTON Pierre
Tous les auteurs :
Shokoohi N, Fsian AN, Thomas JB, Gouton P
Lien Pubmed
Résumé
The spatial resolution of spectral imaging systems is fundamentally constrained by hardware trade-offs, and the availability of large-scale annotated spectral datasets remains limited. This study presents a comprehensive evaluation of super-resolution (SR) methods across interpolation-based, CNN-based, GAN-based, and diffusion-based approaches. Using a synthetic 30-band spectral representation reconstructed from RGB with the MST++ model as a proxy ground truth, we arrange non-adjacent triplets as three-channel PNG inputs to ensure compatibility with existing SR architectures. A unified pipeline enables reproducible evaluation at ×2, ×4, and ×8 scales on 50 unseen images, with performance assessed using PSNR, SSIM, and SAM. Results confirm that bicubic interpolation remains a spectrally reliable baseline; shallow CNNs (SRCNN, FSRCNN) generalize well without fine-tuning; and ESRGAN improves spatial detail at the expense of spectral accuracy. Diffusion models (SR3, ResShift, SinSR), evaluated in a zero-shot setting without spectral-domain adaptation, exhibit unstable performance and require spectrum-aware training to preserve spectral structure effectively. The findings underscore a persistent trade-off between perceptual sharpness and spectral fidelity, highlighting the importance of domain-aware objectives when applying generative SR models to spectral data. This work provides reproducible baselines and a flexible evaluation framework to support future research in spectral image restoration.
Mots clés
RGB-to-spectral reconstruction, diffusion models, generative image restoration, hyperspectral imaging, spectral filter arrays (SFA), spectral imaging, super-resolution (SR)
Référence
Sensors (Basel). 2026 01 20;26(2):
