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Computational Imaging and Sensing Technology
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Computational Imaging and Sensing Technology

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 4564

Special Issue Editors

Dr. Fucai Zhang

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: computational imaging; coherent diffraction imaging; synchrotron X-ray imaging; high resolution transmission electron microscopy
Prof. Dr. Chao Zuo

grade E-Mail Website
Guest Editor
Department of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: computational bio-imaging; noninterferometic phase retrieval; optical information processing; high-speed 3D optical sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Liangcai Cao

E-Mail Website
Guest Editor
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, China
Interests: holographic imaging and display

Special Issue Information

Dear Colleagues,

The field of computational imaging and optical sensing seeks to collectively exploit the capabilities of optics, electronics and advanced mathematical algorithms in realizing imaging capabilities beyond the traditional system based solely on optics. With the widely constructed large-scale light source capacity of short wavelengths, such as synchrotron and free electron lasers, computational imaging is rapidly evolving. Application areas for computational imaging and sensing range from materials science and biology to medical and defense industries. This Special Issue seeks to highlight the latest advances in computational imaging, emphasizing the integration of opto-electric measurement and algorithm development.

This Special Issue will focus on (but will not be limited to) the following topics:

  • Novel lensless imaging methodology
  • Microscopy (holographic, computational)
  • Ptychography and phase imaging
  • Spectral imaging
  • Compressive sensing
  • Ultra-fast imaging
  • Machine learning in computational imaging
  • Structured illumination
  • Quantum computational imaging
  • Depth-resolved and turbid imaging
  • Quantitative fast 3D tomography

Dr. Fucai Zhang
Prof. Dr. Chao Zuo
Prof. Dr. Liangcai Cao
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

  • coherent diffraction imaging
  • ptychography
  • holographic microscopy
  • compressive sensing
  • tomography
  • lensless imaging

Published Papers (3 papers)

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Research

24 pages, 6213 KiB  
Article
Compressive Sensing Imaging Spectrometer for UV-Vis Stellar Spectroscopy: Instrumental Concept and Performance Analysis
by Vanni Nardino, Donatella Guzzi, Cinzia Lastri, Lorenzo Palombi, Giulio Coluccia, Enrico Magli, Demetrio Labate and Valentina Raimondi
Sensors 2023, 23(4), 2269; https://doi.org/10.3390/s23042269 - 17 Feb 2023
Viewed by 1525
Abstract
Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the acquisition of the signal in [...] Read more.
Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the acquisition of the signal in a very compact way, merging acquisition and compression in a single step and, furthermore, offering the capability of using a limited number of detector elements to obtain a reconstructed image with a larger number of pixels. Although the CS paradigm has already been applied in several application domains, from medical diagnostics to microscopy, studies related to space applications are very limited. In this paper, we present and discuss the instrumental concept, optical design, and performances of a CS imaging spectrometer for ultraviolet-visible (UV–Vis) stellar spectroscopy. The instrument—which is pixel-limited in the entire 300 nm–650 nm spectral range—features spectral sampling that ranges from 2.2 nm@300 nm to 22 nm@650 nm, with a total of 50 samples for each spectrum. For data reconstruction quality, the results showed good performance, measured by several quality metrics chosen from those recommended by CCSDS. The designed instrument can achieve compression ratios of 20 or higher without a significant loss of information. A pros and cons analysis of the CS approach is finally carried out, highlighting main differences with respect to a traditional system. Full article
(This article belongs to the Special Issue Computational Imaging and Sensing Technology)
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18 pages, 7833 KiB  
Article
Nyquist Sampling Conditions of Some Diffraction Algorithms with Adjustable Magnification
by Chunzheng Wang, Jianshe Ma, Chao Cai and Ping Su
Sensors 2023, 23(3), 1662; https://doi.org/10.3390/s23031662 - 2 Feb 2023
Viewed by 1275
Abstract
Diffraction algorithms with adjustable magnification are dominant in holographic projection and imaging. However, the algorithms are limited by the Nyquist sampling conditions, and simulation results with inappropriate parameters sometimes appear with aliasing. At present, many diffraction algorithms have been proposed and improved, but [...] Read more.
Diffraction algorithms with adjustable magnification are dominant in holographic projection and imaging. However, the algorithms are limited by the Nyquist sampling conditions, and simulation results with inappropriate parameters sometimes appear with aliasing. At present, many diffraction algorithms have been proposed and improved, but there is a need for an overall analysis of their sampling conditions. In this paper, some classical diffraction algorithms with adjustable magnification are summarized, and their sampling conditions in the case of plane wave or spherical wave illumination are analyzed and compared, which helps to select the appropriate diffraction algorithm according to the specific parameter conditions of the simulation to avoid aliasing. Full article
(This article belongs to the Special Issue Computational Imaging and Sensing Technology)
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15 pages, 1854 KiB  
Article
Learning to Sense for Coded Diffraction Imaging
by Rakib Hyder, Zikui Cai and M. Salman Asif
Sensors 2022, 22(24), 9964; https://doi.org/10.3390/s22249964 - 17 Dec 2022
Viewed by 1054
Abstract
In this paper, we present a framework to learn illumination patterns to improve the quality of signal recovery for coded diffraction imaging. We use an alternating minimization-based phase retrieval method with a fixed number of iterations as the iterative method. We represent the [...] Read more.
In this paper, we present a framework to learn illumination patterns to improve the quality of signal recovery for coded diffraction imaging. We use an alternating minimization-based phase retrieval method with a fixed number of iterations as the iterative method. We represent the iterative phase retrieval method as an unrolled network with a fixed number of layers where each layer of the network corresponds to a single step of iteration, and we minimize the recovery error by optimizing over the illumination patterns. Since the number of iterations/layers is fixed, the recovery has a fixed computational cost. Extensive experimental results on a variety of datasets demonstrate that our proposed method significantly improves the quality of image reconstruction at a fixed computational cost with illumination patterns learned only using a small number of training images. Full article
(This article belongs to the Special Issue Computational Imaging and Sensing Technology)
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