Exposure Invariance in Spectral Reconstruction from RGB Images

Lin, Yi-tun and Finlayson, Graham D. (2019) Exposure Invariance in Spectral Reconstruction from RGB Images. In: 27th Color and Imaging Conference Final Program and Proceedings, 2019-10-21 - 2019-10-25.

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Abstract

In the spectral reconstruction ( SR ) problem, reflectance and/or radiance spectra are recovered from RGB images. Most of the prior art only attempts to solve this problem for fixed exposure conditions, and this limits the usefulness of these approaches (they can work inside the lab but not in the real world). In this paper, we seek methods that work well even when exposure is unknown or varies across an image, namely 'exposure invariance'. We begin by re-examining three main approaches - regression, sparse coding and Deep Neural Networks (DNN) - from a varying exposure viewpoint. All three of these approaches are predominantly implemented assuming a fixed capturing condition. However, the leading sparse coding approach (which is almost the best approach overall) is shown to be exposure-invariant, and this teaches that exposure invariance need not come at the cost of poorer overall performance. This result in turn encouraged us to revisit the regression approach. Remarkably, we show that a very simple root-polynomial regression model - which by construction is exposure-invariant - provides competitive performance without any of the complexity inherent in sparse coding or DNNs.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 13 Dec 2019 01:28
Last Modified: 22 Apr 2020 09:42
URI: https://ueaeprints.uea.ac.uk/id/eprint/73362
DOI: 10.2352/issn.2169-2629.2019.27.51

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