Cluster-based deep convolutional networks for spectral reconstruction from RGB images

Zou, Changzhong; Wei, Minghui

doi:10.1016/j.neucom.2021.08.104

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…In this study, the mean relative error (MRAE) and root mean square error (RMSE) are selected as evaluation metrics for spectral reconstruction [ 31 , 32 ]. MRAE is utilized to measure the average relative error between the reconstructed spectra and the actual spectra, as defined by Equation (1).…”

Section: Methodsmentioning

confidence: 99%

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

Zhao,

Luo,

Wang

et al. 2024

Sensors

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Aircraft failures can result in the leakage of fuel, hydraulic oil, or other lubricants onto the runway during landing or taxiing. Damage to fuel tanks or oil lines during hard landings or accidents can also contribute to these spills. Further, improper maintenance or operational errors may leave oil traces on the runway before take-off or after landing. Identifying oil spills in airport runway videos is crucial to flight safety and accident investigation. Advanced image processing techniques can overcome the limitations of conventional RGB-based detection, which struggles to differentiate between oil spills and sewage due to similar coloration; given that oil and sewage have distinct spectral absorption patterns, precise detection can be performed based on multispectral images. In this study, we developed a method for spectrally enhancing RGB images of oil spills on airport runways to generate HSI images, facilitating oil spill detection in conventional RGB imagery. To this end, we employed the MST++ spectral reconstruction network model to effectively reconstruct RGB images into multispectral images, yielding improved accuracy in oil detection compared with other models. Additionally, we utilized the Fast R-CNN oil spill detection model, resulting in a 5% increase in Intersection over Union (IOU) for HSI images. Moreover, compared with RGB images, this approach significantly enhanced detection accuracy and completeness by 25.3% and 26.5%, respectively. These findings clearly demonstrate the superior precision and accuracy of HSI images based on spectral reconstruction in oil spill detection compared with traditional RGB images. With the spectral reconstruction technique, we can effectively make use of the spectral information inherent in oil spills, thereby enhancing detection accuracy. Future research could delve deeper into optimization techniques and conduct extensive validation in real airport environments. In conclusion, this spectral reconstruction-based technique for detecting oil spills on airport runways offers a novel and efficient approach that upholds both efficacy and accuracy. Its wide-scale implementation in airport operations holds great potential for improving aviation safety and environmental protection.

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Section: Methodsmentioning

confidence: 99%

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

Zhao,

Luo,

Wang

et al. 2024

Sensors

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“…In past studies, spectral reconstruction algorithms can generally be divided into two categories: traditional algorithms that utilize prior knowledge, and data-driven deep learning algorithms [22][23][24]. Traditional methods rely on prior knowledge, including spectral correlation, sparsity, and spatial similarity.…”

Section: Hsi Reconstructionmentioning

confidence: 99%

Spectral Reconstruction from Thermal Infrared Multispectral Image Using Convolutional Neural Network and Transformer Joint Network

Zhao,

Qu,

Wang

et al. 2024

Remote Sensing

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Thermal infrared remotely sensed data, by capturing the thermal radiation characteristics emitted by the Earth’s surface, plays a pivotal role in various domains, such as environmental monitoring, resource exploration, agricultural assessment, and disaster early warning. However, the acquisition of thermal infrared hyperspectral remotely sensed imagery necessitates more complex and higher-precision sensors, which in turn leads to higher research and operational costs. In this study, a novel Convolutional Neural Network (CNN)–Transformer combined block, termed CTBNet, is proposed to address the challenge of thermal infrared multispectral image spectral reconstruction. Specifically, the CTBNet comprises blocks that integrate CNN and Transformer technologies (CTB). Within these CTBs, an improved self-attention mechanism is introduced, which not only considers features across spatial and spectral dimensions concurrently, but also explicitly extracts incremental features from each channel. Compared to other algorithms, the proposed method more closely aligns with the true spectral curves in the reconstruction of hyperspectral images across the spectral dimension. Through a series of experiments, this approach has been proven to ensure robustness and generalizability, outperforming some state-of-the-art algorithms across various metrics.

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“…The user can influence the virtual world via a programmed input interface. In the case of augmented reality, the expression calculation will combine the real-world vision with the virtual world, resulting in a hybrid, where the entire information flow is processed in real time by the VR system (Cheng et al, 2019;Chen et al, 2020;He and Yu, 2019;Huang and Xia, 2020;Zou and Wei, 2021). With a VR experience, key aspects are the user interface, the virtual world presented and users' life experience.…”

Section: Literature Review 21 Virtual Reality Technologymentioning

confidence: 99%

“…, 2019; Chen et al. , 2020; He and Yu, 2019; Huang and Xia, 2020; Zou and Wei, 2021). With a VR experience, key aspects are the user interface, the virtual world presented and users' life experience.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the effectiveness of VR-based product demonstrations featuring items of furniture

Cheng-yao

2022

LHT

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PurposeThis paper aims to present a case study of virtual-reality-based product demonstrations featuring items of furniture. The results will be of use in further design and development of virtual-reality-based product demonstration systems and could also support effective student learning.Design/methodology/approachA new method was introduced to guide the experiment by confirming orthogonal arrays. User interactions were then planned, and a furniture demonstration system was implemented. The experiment comprised two stages. In the evaluation stage, participants were invited to experience the virtual-reality (VR)-based furniture demonstration system and complete a user experience (UX) survey. Taguchi-style robust design methods were used to design orthogonal table experiments and planning and design operation methods were used to implement an experimental display system in order to obtain optimized combinations of control factors and levels. The second stage involved a confirmatory test for the optimized combinations. A pilot questionnaire was first applied to survey demonstration scenarios that are important to customers.FindingsThe author found in terms of furniture products, product interactive display through VR can achieve good user satisfaction through quality design planning. VR can better grasp the characteristics of products than paper catalogs and website catalogs. And VR can better grasp the characteristics of products than online videos. For “interactive inspection”, “function simulation”, “style customization” and “set-out customization” were the most valuable demonstration scenarios for customers. The results of the experiment confirmed that the “overall rating”, “hedonic appeal” and “practical quality” were the three most important optimized operating methods, constituting a benchmark of user satisfaction.Originality/valueThe author found that it is possible to design and build a VR-based furniture demonstration system with a good level of usability when a suitable quality design method is applied. The optimized user interaction indicators and implementation experience for the VR-based product demonstration presented in this study will be of use in further design and development of similar systems.

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Cluster-based deep convolutional networks for spectral reconstruction from RGB images

Cited by 10 publications

References 17 publications

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

Spectral Reconstruction from Thermal Infrared Multispectral Image Using Convolutional Neural Network and Transformer Joint Network

Exploring the effectiveness of VR-based product demonstrations featuring items of furniture

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