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Subject: search.filters.subject.400909 Photonic and electro-optical devices, sensors and systems (excl. communications)

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    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
    (Massey University, 2024-04-09) Chang, Yuan
    Hyperspectral 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.
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    RGB-NIR side window demosaicing : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics, Massey University, Manawatu, New Zealand. EMBARGOED until 29th March 2026.
    (Massey University, 2023) Reid, Dylan
    The review of literature and the current state-of-the-art reveals that the inclusion of near-infrared (NIR) data is extremely useful in agricultural robotics applications. The most suitable method for collecting this data uses a multispectral filter array (MSFA) data collection method. Specifically, the red, green, blue, and near-infrared (RGB-NIR) MSFA proposed by Monno et al. in [1] (see Figure 31). A clear gap in research has been identified in demosaicing methods for this MSFA. The only existing method is a proprietary adaptation of residual demosaicing also proposed by Monno et al. [1]. To further the commercial viability of RGB-NIR MSFAs in agricultural applications, a new demosaicing method is proposed. Called Side Window Demosaicing (SWD), it is an adaptation of the recent side window filtering (SWF) technique [2]. To test this method a demosaicing experiment was designed. The experiment followed the accepted method for assessing demosaicing algorithms; by taking a ground truth image, artificially mosaicing it, and then applying the demosaicing technique to estimate the original image. Three datasets of ground truth images were used: - The TokyoTech hyperspectral dataset was transformed into RGB-NIR multispectral images [1]. - The M-SIFT dataset [3]. - An RGB-NIR dataset collected using a prism camera. The algorithm’s efficacy was measured using MPSNR and SSIM then contrasted against existing literature. It was found to be worse than other state-of-the-art methods that demosaic similar density channels by approximately 20dB MPSNR and 0.01 SSIM. However, the comparison to existing literature is difficult, as almost all literature uses visible range MSFAs, and the inclusion of an NIR channel presents unique spectral correlation challenges. When used in the agricultural context of the problem, the performance of the algorithm improves by up to 11dB. This, coupled with the simplicity and flexibility of the algorithm relative to existing literature, makes SWD an attractively simple first principles approach for data collection in robotic applications.