Explainable spectral super-resolution based on a single RGB image : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy (PhD) in Electronics and Computer Engineering at Massey University, Manawatu, New Zealand

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dc.contributor.advisorDonald , Bailey
dc.contributor.authorChang, Yuanen
dc.date.accessioned2024-04-09T04:55:38Z
dc.date.available2024-04-09T04:55:38Z
dc.date.issued2024-04-09
dc.description.abstractHyperspectral imaging offers fine spectral measurements of target surfaces, finding utility in various fields. However, traditional hyperspectral systems grapple with high-cost issues. On the other hand, conventional RGB cameras, which provide relatively coarse measurements of surface spectra, are widely accessible. Consequently, the recovery of spectral information from RGB images has emerged as a popular approach for low-cost hyperspectral imaging, a venture also known as single-image spectral super-resolution. Yet, existing methods, mostly rooted in deep convolutional neural networks, tend to suffer from limited interpretability. In our research, we propose an explainable method for single-image spectral super-resolution. This method relies on the RGBPQR colour space, a low-dimensional spectral data model representing the spectrum. Leveraging the RGBPQR spectral model, we can transform the spectral reconstruction task into a regression problem. To tackle the metamerism issue, we analysed existing spectral super-resolution networks and discovered that these networks often depend on local textural information as context to mitigate metamerism. Informed by this insight, we utilized features extracted from multiscale local binary patterns as contextual information to design our explainable method. Furthermore, in this study, we discussed the error measurements and loss functions employed in this research area and proposed a new error measurement that can represent performance more accurately. We also endeavoured to put forward a method for quantitatively measuring the ability to resolve metamerism, a critical problem in spectral super-resolution. Through our research, we offered a simple, low-dimensional, and explainable spectral super-resolution solution.
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/69444
dc.publisherMassey Universityen
dc.rightsThe Authoren
dc.subjectHyperspectral imagingen
dc.subjectColorimetryen
dc.subjectData processingen
dc.subjectImaging systemsen
dc.subjectImage qualityen
dc.subjectspectral super-resolutionen
dc.subject.anzsrc400909 Photonic and electro-optical devices, sensors and systems (excl. communications)en
dc.titleExplainable spectral super-resolution based on a single RGB image : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy (PhD) in Electronics and Computer Engineering at Massey University, Manawatu, New Zealanden
thesis.degree.disciplineElectronics and Computer Engineering
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.description.doctoral-citation-abridgedIn this research, Yuan proposed a novel low-dimensional spectral model. Yuan analysed existing spectral super-resolution networks and discovered that these networks often depend on local textural information as context to mitigate metamerism. Informed by this insight, Yuan utilized features extracted from LBP as contextual information to design the explainable method. Yuan offered a simple, low-dimensional, and explainable spectral super-resolution solution.
thesis.description.doctoral-citation-longIn this research, Yuan propose an explainable method for single-image spectral super-resolution. This method relies on a novel low-dimensional spectral data model called RGBPQR. To tackle the metamerism issue, Yuan analysed existing spectral super-resolution networks and discovered that these networks often depend on local textural information as context to mitigate metamerism. Informed by this insight, Yuan utilized features extracted from multiscale local binary patterns as contextual information to design the explainable method. Through this research, Yuan offered a simple, low-dimensional, and explainable spectral super-resolution solution.
thesis.description.name-pronounciationYuan Chang
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