On the optimization of regression-based spectral reconstruction

Lin, Yi-Tun and Finlayson, Graham D. (2021) On the optimization of regression-based spectral reconstruction. Sensors, 21 (16). ISSN 1424-8220

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Abstract

Spectral reconstruction (SR) algorithms attempt to recover hyperspectral information from RGB camera responses. Recently, the most common metric for evaluating the performance of SR algorithms is the Mean Relative Absolute Error (MRAE)—an ℓ 1 relative error (also known as percentage error). Unsurprisingly, the leading algorithms based on Deep Neural Networks (DNN) are trained and tested using the MRAE metric. In contrast, the much simpler regression-based methods (which actually can work tolerably well) are trained to optimize a generic Root Mean Square Error (RMSE) and then tested in MRAE. Another issue with the regression methods is—because in SR the linear systems are large and ill-posed—that they are necessarily solved using regularization. However, hitherto the regularization has been applied at a spectrum level, whereas in MRAE the errors are measured per wavelength (i.e., per spectral channel) and then averaged. The two aims of this paper are, first, to reformulate the simple regressions so that they minimize a relative error metric in training—we formulate both ℓ 2 and ℓ 1 relative error variants where the latter is MRAE—and, second, we adopt a per-channel regularization strategy. Together, our modifications to how the regressions are formulated and solved leads to up to a 14% increment in mean performance and up to 17% in worst-case performance (measured with MRAE). Importantly, our best result narrows the gap between the regression approaches and the leading DNN model to around 8% in mean accuracy.

Item Type: Article
Uncontrolled Keywords: hyperspectral imaging,inverse problem,multispectral imaging,regression,regularization,spectral reconstruction,analytical chemistry,information systems,atomic and molecular physics, and optics,biochemistry,instrumentation,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/1600/1602
Faculty \ School: Faculty of Science > School of Computing Sciences
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Depositing User: LivePure Connector
Date Deposited: 01 Sep 2021 00:52
Last Modified: 13 Sep 2021 00:06
URI: https://ueaeprints.uea.ac.uk/id/eprint/81259
DOI: 10.3390/s21165586

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