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Hyperspectral/Multispectral Sensing Technologies for Spectral Cameras and Image Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 10 April 2025 | Viewed by 835

Special Issue Editors


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Guest Editor
Departamento de Ingeniería Industrial, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Spain
Interests: optical camera communication; visible light communications; spectral signature multiplexing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
Interests: optical wireless communications; VLC/OCC
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Spectral imaging, enabled by spectral cameras and optical sensors, merges the capabilities of digital imaging and spectroscopy by capturing the geometric image across multiple narrow spectral bands, expanding to cover visible, near-infrared, and shortwave infrared spectrums. This approach enables the detection of optical characteristics in objects that are typically unseen by traditional cameras or the human eye. These spectral attributes directly correspond to the chemical composition of an object, facilitating tasks such as object detection, identification, classification, segmentation, and enhanced color characterization. This sensing technology has emerged as a powerful tool across various fields including remote sensing, agriculture, biomedical imaging, and industrial inspection.

This Special Issue attempts to address recent advancements in spectral camera technologies, focusing on both hyperspectral and multispectral imaging systems. It covers key aspects such as spectral camera design, spectral imaging, spatial resolution, optical camera communication, and signal processing, along with their advantages and limitations. Related sensing applications and techniques of spectral cameras in various fields can also be covered in this Special Issue, emphasizing the significance of this technology in environmental monitoring, precision agriculture, medical diagnosis, material characterization, and joint communications and sensing.

Dr. Julio Francisco Rufo Torres
Prof. Dr. Jose Alberto Rabadan Borges
Guest Editors

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Keywords

  • spectral cameras
  • hyperspectral/multispectral imaging
  • spectral imaging
  • spatial and spectral resolution
  • optical camera communications
  • imaging modalities
  • optical wireless sensor network
  • spectral signature multiplexing

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Published Papers (1 paper)

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Research

17 pages, 1961 KiB  
Article
Mask-Guided Spatial–Spectral MLP Network for High-Resolution Hyperspectral Image Reconstruction
by Xian-Hua Han, Jian Wang and Yen-Wei Chen
Sensors 2024, 24(22), 7362; https://doi.org/10.3390/s24227362 - 18 Nov 2024
Viewed by 539
Abstract
Hyperspectral image (HSI) reconstruction is a critical and indispensable step in spectral compressive imaging (CASSI) systems and directly affects our ability to capture high-quality images in dynamic environments. Recent research has increasingly focused on deep unfolding frameworks for HSI reconstruction, showing notable progress. [...] Read more.
Hyperspectral image (HSI) reconstruction is a critical and indispensable step in spectral compressive imaging (CASSI) systems and directly affects our ability to capture high-quality images in dynamic environments. Recent research has increasingly focused on deep unfolding frameworks for HSI reconstruction, showing notable progress. However, these approaches have to break the optimization task into two sub-problems, solving them iteratively over multiple stages, which leads to large models and high computational overheads. This study presents a simple yet effective method that passes the degradation information (sensing mask) through a deep learning network to disentangle the degradation and the latent target’s representations. Specifically, we design a lightweight MLP block to capture non-local similarities and long-range dependencies across both spatial and spectral domains, and investigate an attention-based mask modelling module to achieve the spatial–spectral-adaptive degradation representationthat is fed to the MLP-based network. To enhance the information flow between MLP blocks, we introduce a multi-level fusion module and apply reconstruction heads to different MLP features for deeper supervision. Additionally, we combine the projection loss from compressive measurements with reconstruction loss to create a dual-domain loss, ensuring consistent optical detection during HS reconstruction. Experiments on benchmark HS datasets show that our method outperforms state-of-the-art approaches in terms of both reconstruction accuracy and efficiency, reducing computational and memory costs. Full article
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