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Computational Spectral Imaging
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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: closed (20 October 2021) | Viewed by 16013

Special Issue Editors

Dr. Mauro Dalla Mura

E-Mail Website
Guest Editor
GIPSA-Lab, Grenoble Institute of Technology, 38402 Saint Martin d'Hères, France
Interests: remote sensing; image processing; signal processing; machine learning; mathematical morphology; data fusion; multivariate data analysis; hyperspectral imaging; pansharpening
Special Issues, Collections and Topics in MDPI journals
Dr. Henry Arguello Fuentes

E-Mail 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

E-Mail 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 submissions that pass pre-check are 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 2600 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 (6 papers)

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31 pages, 6554 KiB  
Article
Coded Aperture Hyperspectral Image Reconstruction
by Ignacio García-Sánchez, Óscar Fresnedo, José P. González-Coma and Luis Castedo
Sensors 2021, 21(19), 6551; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196551 - 30 Sep 2021
Cited by 2 | Viewed by 2117
Abstract
In this work, we study and analyze the reconstruction of hyperspectral images that are sampled with a CASSI device. The sensing procedure was modeled with the help of the CS theory, which enabled efficient mechanisms for the reconstruction of the hyperspectral images from [...] Read more.
In this work, we study and analyze the reconstruction of hyperspectral images that are sampled with a CASSI device. The sensing procedure was modeled with the help of the CS theory, which enabled efficient mechanisms for the reconstruction of the hyperspectral images from their compressive measurements. In particular, we considered and compared four different type of estimation algorithms: OMP, GPSR, LASSO, and IST. Furthermore, the large dimensions of hyperspectral images required the implementation of a practical block CASSI model to reconstruct the images with an acceptable delay and affordable computational cost. In order to consider the particularities of the block model and the dispersive effects in the CASSI-like sensing procedure, the problem was reformulated, as well as the construction of the variables involved. For this practical CASSI setup, we evaluated the performance of the overall system by considering the aforementioned algorithms and the different factors that impacted the reconstruction procedure. Finally, the obtained results were analyzed and discussed from a practical perspective. Full article
(This article belongs to the Special Issue Computational Spectral Imaging)
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22 pages, 4603 KiB  
Article
Multitrack Compressed Sensing for Faster Hyperspectral Imaging
by Sharvaj Kubal, Elizabeth Lee, Chor Yong Tay and Derrick Yong
Sensors 2021, 21(15), 5034; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155034 - 24 Jul 2021
Viewed by 1971
Abstract
Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) [...] Read more.
Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ∼10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times. Full article
(This article belongs to the Special Issue Computational Spectral Imaging)
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23 pages, 18152 KiB  
Article
Reflectance Estimation from Multispectral Linescan Acquisitions under Varying Illumination—Application to Outdoor Weed Identification
by Anis Amziane, Olivier Losson, Benjamin Mathon, Aurélien Dumenil and Ludovic Macaire
Sensors 2021, 21(11), 3601; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113601 - 21 May 2021
Cited by 5 | Viewed by 2058
Abstract
To reduce the amount of herbicides used to eradicate weeds and ensure crop yields, precision spraying can effectively detect and locate weeds in the field thanks to imaging systems. Because weeds are visually similar to crops, color information is not sufficient for effectively [...] Read more.
To reduce the amount of herbicides used to eradicate weeds and ensure crop yields, precision spraying can effectively detect and locate weeds in the field thanks to imaging systems. Because weeds are visually similar to crops, color information is not sufficient for effectively detecting them. Multispectral cameras provide radiance images with a high spectral resolution, thus the ability to investigate vegetated surfaces in several narrow spectral bands. Spectral reflectance has to be estimated in order to make weed detection robust against illumination variation. However, this is a challenge when the image is assembled from successive frames that are acquired under varying illumination conditions. In this study, we present an original image formation model that considers illumination variation during radiance image acquisition with a linescan camera. From this model, we deduce a new reflectance estimation method that takes illumination at the frame level into account. We experimentally show that our method is more robust against illumination variation than state-of-the-art methods. We also show that the reflectance features based on our method are more discriminant for outdoor weed detection and identification. Full article
(This article belongs to the Special Issue Computational Spectral Imaging)
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19 pages, 8741 KiB  
Article
Double Ghost Convolution Attention Mechanism Network: A Framework for Hyperspectral Reconstruction of a Single RGB Image
by Wenju Wang and Jiangwei Wang
Sensors 2021, 21(2), 666; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020666 - 19 Jan 2021
Cited by 8 | Viewed by 3988
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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19 pages, 3444 KiB  
Article
A Microwave Three-Dimensional Imaging Method Based on Optimal Wave Spectrum Reconstruction
by Yan Zhang, Baoping Wang, Yang Fang and Zuxun Song
Sensors 2020, 20(24), 7306; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247306 - 19 Dec 2020
Cited by 1 | Viewed by 1547
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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13 pages, 1749 KiB  
Letter
Scaling-Based Two-Step Reconstruction in Full Polarization-Compressed Hyperspectral Imaging
by Axin Fan, Tingfa Xu, Xi Wang, Chang Xu and Yuhan Zhang
Sensors 2020, 20(24), 7120; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247120 - 11 Dec 2020
Cited by 6 | Viewed by 2003
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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