Distance evaluation on spectral reconstruction.

Authors

Antony Madaleno, Hermine Chatoux, Noël Richard, Franck Marzani

Published in

Journal of the Optical Society of America. A, Optics, image science, and vision. Volume 43. Issue 7. Pages 1136-1151. Jul 01, 2026.

Abstract

Spectral distance and similarity measures inherently bias optimization toward spectral shape or total radiance. In this work, we analyze how such biases affect reconstruction outcomes by comparing several commonly used metrics, including SAM, SGA, SCA, SID, the Euclidean distance, and the Kullback-Leibler pseudo-divergence (KLPD). Distances are evaluated through a spectral reconstruction task based on distance-driven optimization. By exploiting the KLPD decomposition into shape and radiance terms, our results provide a finer interpretation of reconstruction errors and reveal how each metric biases the solution space. This study highlights the relevance of the KLPD for analyzing and comparing spectral distance measures in reconstruction problems.

PMID:
42863043
Bibliographic data and abstract were imported from PubMed on 13 Oct 2026.

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