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Special Issue "Computational Spectral Imaging"

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

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editors

Dr. Mauro Dalla Mura
Website
Guest Editor
Grenoble Images Parole Signals Automatics Laboratory, Grenoble Institute of Technology, Saint Martin d'Hères, France;
Tokyo Tech World Research Hub Initiative (WRHI), School of Computing, Tokyo Institute of Technology, Tokyo, Japan
Interests: computational imaging; image processing; signal processing; remote sensing; data fusion; hyperspectral imaging; pansharpening
Special Issues and Collections in MDPI journals
Dr. Henry Arguello Fuentes
Website
Guest Editor
High Dimensional Signal Processing Research Group Department of Systems Engineering and Informatics, Universidad Industrial de Santander Bucaramanga, Colombia
Interests: Optical and Computational Imaging , High-dimensional Signal Coding and Processing, Deep Learning Based Optimization of Optical Imaging Systems, Compressive Spectral and Depth Imaging, Sparse and Low rank representation.
Dr. Shunsuke Ono
Website
Guest Editor
Mathematical Data Informatics Laboratory Department of Computer Science School of Computing, Tokyo Institute of Technology Kanagawa, Japan
Interests: Signal Processing, Computational Imaging, Hyperspectral Imaging and Fusion, Mathematical Optimization

Special Issue Information

Dear Colleagues,

Computational spectral imaging devices acquire multi- and hyper-spectral images of a scene by leveraging image and signal processing techniques. Examples of computational imaging devices are multispectral sensors based on spectral filter arrays, hyperspectral cameras based on compressed sensing, Fourier transform spectrometers, light field cameras, and multimodal sensors. Computational systems overcome the limits of conventional imaging devices allowing, for example, to acquire images that simultaneously sample the spatial, depth, and spectral information of a scene (i.e., snapshot), achieve a higher spatial and spectral resolution, and even to access additional information of a scene (e.g., depth).
However, computational devices require typically more complex processing of the acquisitions, relying on advanced signal and image processing techniques (e.g., compressed sensing, inverse problems for image reconstruction, demosaicing, and image fusion).
This Special Issue focuses on computational approaches for the acquisition of spectral images, comprising topics such as:

  • Diffractive optics computational imaging
  • Codified spectral and depth imaging systems
  • Learning-based optimization of optical imaging systems 
  • Super-resolution for spectral imaging
  • Compressed sensing in hyperspectral imaging
  • Processing of spectral snapshot acquisition devices
  • Multispectral sensing based on color and spectral filter arrays
  • Demosaicing
  • Fourier transform interferometric imaging
  • Fusion of multimodal sensors (pansharpening and hyperspectral-multispectral fusion)
  • Multi- and hyper-spectral light field cameras
  • Computational techniques for the analysis of multispectral and hyperspectral images
  • Conception and design of computational spectral imaging devices
  • Unconventional optical imaging systems
  • Applications of computational spectral imaging devices in fields such as remote sensing, biomedical imagery, security, and defense.

Dr. Mauro Dalla Mura
Dr. Henry Arguello Fuentes
Dr. Shunsuke Ono
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Computational imaging
  • Hyperspectral and multispectral imaging
  • Compressed sensing imaging
  • Spectral filter arrays
  • Sensor fusion
  • Unconventional imaging sensors

Published Papers (3 papers)

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Open AccessArticle
Double Ghost Convolution Attention Mechanism Network: A Framework for Hyperspectral Reconstruction of a Single RGB Image
Sensors 2021, 21(2), 666; https://doi.org/10.3390/s21020666 - 19 Jan 2021
Abstract
Current research on the reconstruction of hyperspectral images from RGB images using deep learning mainly focuses on learning complex mappings through deeper and wider convolutional neural networks (CNNs). However, the reconstruction accuracy of the hyperspectral image is not high and among other issues [...] Read more.
Current research on the reconstruction of hyperspectral images from RGB images using deep learning mainly focuses on learning complex mappings through deeper and wider convolutional neural networks (CNNs). However, the reconstruction accuracy of the hyperspectral image is not high and among other issues the model for generating these images takes up too much storage space. In this study, we propose the double ghost convolution attention mechanism network (DGCAMN) framework for the reconstruction of a single RGB image to improve the accuracy of spectral reconstruction and reduce the storage occupied by the model. The proposed DGCAMN consists of a double ghost residual attention block (DGRAB) module and optimal nonlocal block (ONB). DGRAB module uses GhostNet and PRELU activation functions to reduce the calculation parameters of the data and reduce the storage size of the generative model. At the same time, the proposed double output feature Convolutional Block Attention Module (DOFCBAM) is used to capture the texture details on the feature map to maximize the content of the reconstructed hyperspectral image. In the proposed ONB, the Argmax activation function is used to obtain the region with the most abundant feature information and maximize the most useful feature parameters. This helps to improve the accuracy of spectral reconstruction. These contributions enable the DGCAMN framework to achieve the highest spectral accuracy with minimal storage consumption. The proposed method has been applied to the NTIRE 2020 dataset. Experimental results show that the proposed DGCAMN method outperforms the spectral accuracy reconstructed by advanced deep learning methods and greatly reduces storage consumption. Full article
(This article belongs to the Special Issue Computational Spectral Imaging)
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Open AccessArticle
A Microwave Three-Dimensional Imaging Method Based on Optimal Wave Spectrum Reconstruction
Sensors 2020, 20(24), 7306; https://doi.org/10.3390/s20247306 - 19 Dec 2020
Abstract
Limited by the Shannon–Nyquist sampling law, the number of antenna elements and echo signal data of the traditional microwave three-dimensional (3D) imaging system are extremely high. Compressed sensing imaging methods based on sparse representation of target scene can reduce the data sampling rate, [...] Read more.
Limited by the Shannon–Nyquist sampling law, the number of antenna elements and echo signal data of the traditional microwave three-dimensional (3D) imaging system are extremely high. Compressed sensing imaging methods based on sparse representation of target scene can reduce the data sampling rate, but the dictionary matrix of these methods takes a lot of memory, and the imaging has poor quality for continuously distributed targets. For the above problems, a microwave 3D imaging method based on optimal wave spectrum reconstruction and optimization with target reflectance gradient is proposed in this paper. Based on the analysis of the spatial distribution characteristics of the target echo in the frequency domain, this method constructs an orthogonal projection reconstruction model for the wavefront to realize the optimal reconstruction of the target wave spectrum. Then, the inverse Fourier transform of the optimal target wave spectrum is optimized according to the law of the target reflectance gradient distribution. The proposed method has the advantages of less memory space and less computation time. What is more, the method has a better imaging quality for the continuously distributed target. The computer simulation experiment and microwave anechoic chamber measurement experiment verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Computational Spectral Imaging)
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Open AccessLetter
Scaling-Based Two-Step Reconstruction in Full Polarization-Compressed Hyperspectral Imaging
Sensors 2020, 20(24), 7120; https://doi.org/10.3390/s20247120 - 11 Dec 2020
Abstract
Polarized hyperspectral images can reflect the rich physicochemical characteristics of targets. Meanwhile, the contained plentiful information also brings great challenges to signal processing. Although compressive sensing theory provides a good idea for image processing, the simplified compression imaging system has difficulty in reconstructing [...] Read more.
Polarized hyperspectral images can reflect the rich physicochemical characteristics of targets. Meanwhile, the contained plentiful information also brings great challenges to signal processing. Although compressive sensing theory provides a good idea for image processing, the simplified compression imaging system has difficulty in reconstructing full polarization information. Focused on this problem, we propose a two-step reconstruction method to handle polarization characteristics of different scales progressively. This paper uses a quarter-wave plate and a liquid crystal tunable filter to achieve full polarization compression and hyperspectral imaging. According to their numerical features, the Stokes parameters and their modulation coefficients are simultaneously scaled. The first Stokes parameter is reconstructed in the first step based on compressive sensing. Then, the last three Stokes parameters with similar order of magnitude are reconstructed in the second step based on previous results. The simulation results show that the two-step reconstruction method improves the reconstruction accuracy by 7.6 dB for the parameters that failed to be reconstructed by the non-optimized method, and reduces the reconstruction time by 8.25 h without losing the high accuracy obtained by the current optimization method. This feature scaling method provides a reference for the fast and high-quality reconstruction of physical quantities with obvious numerical differences. Full article
(This article belongs to the Special Issue Computational Spectral Imaging)
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