Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures

Wisotzky EL, Wallburg L, Hilsmann A, Eisert P, Wittenberg T, Göb S (2024)

Publication Type: Conference contribution

Publication year: 2024

Publisher: Science and Technology Publications, Lda

Book Volume: 3

Pages Range: 541-550

Conference Proceedings Title: Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

Event location: Rome, ITA

DOI: 10.5220/0012392300003660

Abstract

Neural network architectures for image demosaicing have been become more and more complex. This results in long training periods of such deep networks and the size of the networks is huge. These two factors prevent practical implementation and usage of the networks in real-time platforms, which generally only have limited resources. This study investigates the effectiveness of neural network architectures in hyperspectral image demosaicing. We introduce a range of network models and modifications, and compare them with classical interpolation methods and existing reference network approaches. The aim is to identify robust and efficient performing network architectures. Our evaluation is conducted on two datasets, ”SimpleData” and ”SimRealData,” representing different degrees of realism in multispectral filter array (MSFA) data. The results indicate that our networks outperform or match reference models in both datasets demonstrating exceptional performance. Notably, our approach focuses on achieving correct spectral reconstruction rather than just visual appeal, and this emphasis is supported by quantitative and qualitative assessments. Furthermore, our findings suggest that efficient demosaicing solutions, which require fewer parameters, are essential for practical applications. This research contributes valuable insights into hyperspectral imaging and its potential applications in various fields, including medical imaging.

Authors with CRIS profile

Thomas Wittenberg

Involved external institutions

Fraunhofer-Institut für Nachrichtentechnik (Heinrich-Hertz-Institut FhG HHI) DE Germany (DE) Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Wisotzky, E.L., Wallburg, L., Hilsmann, A., Eisert, P., Wittenberg, T., & Göb, S. (2024). Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures. In Petia Radeva, Antonino Furnari, Kadi Bouatouch, A. Augusto Sousa (Eds.), Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 541-550). Rome, ITA: Science and Technology Publications, Lda.

MLA:

Wisotzky, Eric L., et al. "Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures." Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2024, Rome, ITA Ed. Petia Radeva, Antonino Furnari, Kadi Bouatouch, A. Augusto Sousa, Science and Technology Publications, Lda, 2024. 541-550.

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