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Applied Sciences
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Computational Sensing and Imaging II
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Computational Sensing and Imaging II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 1609

Special Issue Editor

Prof. Dr. Hoon Yoo

E-Mail Website
Guest Editor
Department of Electronics Engineering, Sangmyung University, 20 Hongjimoon-2gil, Seoul 030031, Korea
Interests: image processing; 3D imaging; computational reconstruction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational sensing and imaging play a major role in imaging techniques and have many applications in areas of medical imaging, 3D imaging, lensless imaging, synthetic aperture radar imaging, seismic imaging, ultrasound imaging, and so on. In addition, extensive research has accelerated the sensing and imaging performance of those applications in the last few years. Specifically, this Special Issue is focused on image processing to obtain high-quality images and to remedy various problems to deteriorate the sensing and imaging performance of physical or optical devices in terms of image quality, imaging speed, and functionality.

This Special Issue aims to broadly engage the communities of image processing and signal sensing to provide a forum for researchers and engineers related to this rapidly developing field, allowing them to share their novel and original research on the topic of computational sensing and imaging. Survey papers addressing relevant topics are also welcome.

Prof. Dr. Hoon Yoo
Guest Editor

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. Applied Sciences 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 2400 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 photography for 3D imaging
  • depth estimation and three-dimensional sensing
  • medical imaging (CT/MRI/PET image reconstruction)
  • image restoration and denoising
  • image registration and super-resolution imaging
  • high-speed imaging systems and bandwidth reduction
  • computational sensing for advanced driver assistance systems (ADAS)
  • synthetic aperture radar (SAR) imaging
  • seismic imaging
  • ultrasound imaging
  • computational sensing for advanced image signal processor (ISP)
  • deep learning for image reconstruction
  • remote sensing and UAV image processing
  • under-water imaging and dehazing

Published Papers (2 papers)

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23 pages, 14529 KiB  
Article
Doppler Factor in the Omega-k Algorithm for Pulsed and Continuous Wave Synthetic Aperture Radar Raw Data Processing
by Jhohan Jancco-Chara, Facundo Palomino-Quispe, Roger Jesus Coaquira-Castillo, Julio Cesar Herrera-Levano and Ruben Florez
Appl. Sci. 2024, 14(1), 320; https://doi.org/10.3390/app14010320 - 29 Dec 2023
Viewed by 513
Abstract
Synthetic aperture radar (SAR) raw data do not have a direct application; therefore, SAR raw signal processing algorithms are used to generate images that are used for various required applications. Currently, there are several algorithms focusing SAR raw data such as the range-Doppler [...] Read more.
Synthetic aperture radar (SAR) raw data do not have a direct application; therefore, SAR raw signal processing algorithms are used to generate images that are used for various required applications. Currently, there are several algorithms focusing SAR raw data such as the range-Doppler algorithm, Chirp Scaling algorithm, and Omega-k algorithm, with these algorithms being the most used and traditional in SAR raw signal processing. The most prominent algorithm that operates in the frequency domain for focusing SAR raw data obtained by a synthetic aperture radar with large synthetic apertures is the Omega-k algorithm, which operates in the two-dimensional frequency domain; therefore, in this paper, we used the Omega-k algorithm to produce SAR images and modify the Omega-k algorithm by adding the Doppler factor to improve the accuracy of SAR raw data processing obtained by the continuous wave and pulsed frequency modulated linear frequency modulated radar system from the surfaces of interest. On the other hand, for the case of unmanned aerial vehicle-borne linear frequency modulated continuous wave (LFM-CW) SAR systems, we added motion compensation to the modified Omega-k algorithm. Finally, the testing and validation of the developed Omega-k algorithm used simulated and real SAR raw data for both pulsed synthetic aperture and continuous wave radars. The real SAR raw data used for the validation of the modified Omega-k algorithm were the raw data obtained by the micro advanced synthetic aperture radar (MicroASAR) system, which is an LFM-CW synthetic aperture radar installed on board an unmanned aerial system and the raw data obtained by European remote sensing (ERS-2) satellite with a synthetic aperture radar installed. Full article
(This article belongs to the Special Issue Computational Sensing and Imaging II)
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13 pages, 9980 KiB  
Article
Fast Numerical Reconstruction of Integral Imaging Based on a Determined Interval Mapping
by Heemin Choi, Nam Kim and Hoonjong Kang
Appl. Sci. 2023, 13(12), 6942; https://doi.org/10.3390/app13126942 - 08 Jun 2023
Viewed by 679
Abstract
In this paper, a fast numerical reconstruction of the integral imaging based on a determined interval mapping is proposed. To reduce the computation time, the proposed method employs the determined interval mapping instead of the use of magnification. In the numerical reconstruction procedure, [...] Read more.
In this paper, a fast numerical reconstruction of the integral imaging based on a determined interval mapping is proposed. To reduce the computation time, the proposed method employs the determined interval mapping instead of the use of magnification. In the numerical reconstruction procedure, the acquired elemental image array (EIA) from the 3D object is displayed. The flipped elemental image (EI)s are numerically formed by the virtual pinhole array. Then, the determined interval depending on the reconstruction plane is calculated and applied to each flipped EI. These flipped EIs are shifted to match the determined interval at the reconstruction plane and superimposed together. After this superimposed image is divided by the number of the superposition, the position error between the location of the shifted EI and the pixel position of the reconstruction plane is corrected by interpolation. As a result, the refocused image depending on the reconstruction plane can be reconstructed rapidly. From the experimental result, we confirmed that the proposed method largely decreased the computation time compared with the conventional method. In addition, we verified that the quality of the reconstruction by the proposed method is higher than the conventional method by the use of the structural similarity index method. Full article
(This article belongs to the Special Issue Computational Sensing and Imaging II)
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