Enhancing deep spectral super-resolution from RGB images by enforcing the metameric constraint
Stiebel T, Seltsam P, Merhof D (2020)
Publication Type: Conference contribution
Publication year: 2020
Publisher: SciTePress
Book Volume: 4
Pages Range: 57-66
Conference Proceedings Title: VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Event location: Valletta, MLT
ISBN: 9789897584022
Abstract
The task of spectral signal reconstruction from RGB images requires to solve a heavily underconstrained set of equations. In recent work, deep learning has been applied to solve this inherently difficult problem. Based on a given training set of corresponding RGB images and spectral images, a neural network is trained to learn an optimal end-to-end mapping. However, in such an approach no additional knowledge is incorporated into the networks prediction. We propose and analyze methods for incorporating prior knowledge based on the idea, that when reprojecting any reconstructed spectrum into the camera RGB space it must be (ideally) identical to the originally measured camera signal. It is therefore enforced, that every reconstruction is at least a metamer of the ideal spectrum with respect to the observed signal and observer. This is the one major constraint that any reconstruction should fulfil to be physically plausible, but has been neglected so far.
Involved external institutions
Germany (DE)How to cite
APA:
Stiebel, T., Seltsam, P., & Merhof, D. (2020). Enhancing deep spectral super-resolution from RGB images by enforcing the metameric constraint. In Giovanni Maria Farinella, Petia Radeva, Jose Braz (Eds.), VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 57-66). Valletta, MLT: SciTePress.
MLA:
Stiebel, Tarek, Philipp Seltsam, and Dorit Merhof. "Enhancing deep spectral super-resolution from RGB images by enforcing the metameric constraint." Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, Valletta, MLT Ed. Giovanni Maria Farinella, Petia Radeva, Jose Braz, SciTePress, 2020. 57-66.
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