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Electronics
Special Issues
Computational Imaging and Its Application
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Computational Imaging and Its Application

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 1520

Special Issue Editors

Dr. Xuan Li

E-Mail Website
Guest Editor
School of Optoelectronic Engineering, Xidian University, Xi’an 710071, China Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China
Interests: computational imaging; polarization imaging; 3D imaging and machine vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaopeng Shao

E-Mail Website
Guest Editor
1. School of Optoelectronic Engineering, Xidian University, Xi’an 710071, China
2. Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China
Interests: computational imaging; optical instrumentation, optical image processing and pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Teli Xi

E-Mail Website
Guest Editor
School of Optoelectronic Engineering, Xidian University, Xi’an 710071, China
Interests: imaging through scattering media; computational optical imaging system design; quantitative phase imaging techniques and applications
Dr. Jinpeng Liu

E-Mail Website
Guest Editor
School of Optoelectronic Engineering, Xidian University, Xi’an 710071, China
Interests: imaging through scattering media; biomedical imaging
Dr. Yangyundou Wang

E-Mail Website
Guest Editor
Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China
Interests: lensless optics; deep learning

Special Issue Information

Dear Colleagues,

After many years of development, the techniques in computational imaging have caused profound societal and financial effects. With the rapid changes and developments in application environments and detection technologies, traditional methods are unable to provide high-quality imaging needs. Providing strong and effective methods to ensure the resolution, clarity, efficiency, and robustness of imaging in different application scenarios is becoming important, both in academia and industry. In particular, it is urgent to explore and develop new imaging technologies with higher resolutions, smaller optical system sizes, stronger adaptability, longer detection distances, and larger fields of view. Moreover, there are still many open problems in this area that need to be studied more deeply. Therefore, research on advanced techniques in computational imaging and its applications can bring about countless potential improvements to our world.

The objective of this Special Issue is to attract the latest research results dedicated to computational imaging and its applications. This Special Issue will bring leading researchers and developers from both academia and industry together to present their novel research on computational imaging and its applications. The submitted papers will be peer-reviewed and will be selected based on their quality and relevance to the main themes of this Special Issue.

The scope includes, but is not limited to:

(1) 3D imaging;

(2) Polarization imaging;

(3) Scattering imaging;

(4) Wave front Coding Imaging;

(5) Phase imaging;

(6) Biomedical imaging;

(7) Computational imaging with deep learning

(8) Lensless optics;

(9) Fiber optic sensing;

(10) Optical frequency comb and its application.

Dr. Xuan Li
Prof. Dr. Xiaopeng Shao
Dr. Teli Xi
Dr. Jinpeng Liu
Dr. Yangyundou Wang
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. Electronics 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 imaging
  • phase
  • polarization
  • 3D
  • coding
  • digital holography
  • wave front sensing
  • deep learning
  • super-resolution

Published Papers (2 papers)

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Research

11 pages, 5843 KiB  
Article
Controllable Spatial Filtering Method in Lensless Imaging
by Jae-Young Jang and Myungjin Cho
Electronics 2024, 13(7), 1184; https://doi.org/10.3390/electronics13071184 - 23 Mar 2024
Viewed by 374
Abstract
We propose a method for multiple-depth extraction in diffraction grating imaging. A diffraction grating can optically generate a diffraction image array (DIA) having parallax information about a three-dimensional (3D) object. The optically generated DIA has the characteristic of forming images periodically, and the [...] Read more.
We propose a method for multiple-depth extraction in diffraction grating imaging. A diffraction grating can optically generate a diffraction image array (DIA) having parallax information about a three-dimensional (3D) object. The optically generated DIA has the characteristic of forming images periodically, and the period depends on the depth of the object, the wavelength of the light source, and the grating period of the diffraction grating. The depth image can be extracted through the convolution of the DIA and the periodic delta function array. Among the methods for extracting depth images through the convolution characteristics of a parallax image array (PIA) and delta function array, an advanced spatial filtering method for the controllable extract of multiple depths (CEMD) has been studied as one of the reconstruction methods. And that possibility was confirmed through a lens-array-based computational simulation. In this paper, we aim to perform multiple-depth extraction by applying the CEMD method to a DIA obtained optically through a diffraction grating. To demonstrate the application of the CEMD in diffraction grating imaging, a theoretical analysis is performed to apply the CEMD in diffraction grating imaging; the DIA is acquired optically, and the spatial filtering process is performed through computational methods and then compared with the conventional single-depth extraction method in diffraction grating imaging. The application of the CEMD to DIA enables the simultaneous reconstruction of images corresponding to multiple depths through a single spatial filtering process. To the best of our knowledge, this is the first research on the extraction of multiple-depth images in diffraction grating imaging. Full article
(This article belongs to the Special Issue Computational Imaging and Its Application)
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21 pages, 1025 KiB  
Article
Weakly Supervised Cross-Domain Person Re-Identification Algorithm Based on Small Sample Learning
by Huiping Li, Yan Wang, Lingwei Zhu, Wenchao Wang, Kangning Yin, Ye Li and Guangqiang Yin
Electronics 2023, 12(19), 4186; https://doi.org/10.3390/electronics12194186 - 09 Oct 2023
Viewed by 834
Abstract
This paper proposes a weakly supervised cross-domain person re-identification (Re-ID) method based on small sample data. In order to reduce the cost of data collection and annotation, the model design focuses on extracting and abstracting the information contained in the data under limited [...] Read more.
This paper proposes a weakly supervised cross-domain person re-identification (Re-ID) method based on small sample data. In order to reduce the cost of data collection and annotation, the model design focuses on extracting and abstracting the information contained in the data under limited conditions. In this paper, we focus on the problems of strong data dependence, weak cross-domain capability and low accuracy in Re-ID in weakly supervised scenarios. Our contributions are as follows: first, we implement a joint training framework with the help of small sample learning and cross-domain migration for Re-ID. Second, with the help of residual compensation and fusion attention module, the RCFA module is designed, and the model framework is built on this basis to improve the cross-domain ability of the model. Third, to solve the problem of low accuracy caused by insufficient data coverage of small samples, a fusion of shallow features and deep features is designed to enable the model to weighted fusion of shallow detail information and deep semantic information. Finally, by selecting different camera images in Market1501 dataset and DukeMTMC-reID dataset as small samples, respectively, and introducing another dataset data for joint training, we demonstrate the feasibility of this joint training framework, which can perform weakly supervised cross-domain Re-ID based on small sample data. Full article
(This article belongs to the Special Issue Computational Imaging and Its Application)
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