Back to Search
Start Over
On the Optimization of Regression-Based Spectral Reconstruction
- Source :
- Sensors, Vol 21, Iss 5586, p 5586 (2021), Sensors (Basel, Switzerland), Sensors, Volume 21, Issue 16
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
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 ℓ1 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 ℓ2 and ℓ1 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.
- Subjects :
- Mean squared error
hyperspectral imaging
TP1-1185
Biochemistry
Regularization (mathematics)
Article
spectral reconstruction
Analytical Chemistry
Approximation error
multispectral imaging
Electrical and Electronic Engineering
Instrumentation
Mathematics
Phantoms, Imaging
Chemical technology
Linear system
Contrast (statistics)
Inverse problem
Atomic and Molecular Physics, and Optics
Regression
regularization
Metric (mathematics)
inverse problem
regression
Neural Networks, Computer
Algorithm
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 5586
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....aca82c49ead337348876a770716ce6ef