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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 5084

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

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Published Papers (6 papers)

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Research

16 pages, 2015 KiB  
Article
A Study on the Simple Encryption of QR Codes Using Random Numbers
by Iori Okubo, Seiya Ono, Hyun-Woo Kim, Myungjin Cho and Min-Chul Lee
Electronics 2024, 13(15), 3003; https://doi.org/10.3390/electronics13153003 - 30 Jul 2024
Viewed by 537
Abstract
Recently, with the widespread adoption of quick response (QR) code payments, there have been incidents of unauthorized use of QR codes presented at the time of payment, due to theft or duplication. As a countermeasure, conventional QR code payment systems are designed to [...] Read more.
Recently, with the widespread adoption of quick response (QR) code payments, there have been incidents of unauthorized use of QR codes presented at the time of payment, due to theft or duplication. As a countermeasure, conventional QR code payment systems are designed to update the QR code periodically. However, there is a problem: it is possible to make a payment using an illegally obtained QR code until the update. Therefore, it is necessary to encrypt the QR code itself to prevent its duplication. The objective of this research is to prevent fraudulent use of QR payments by combining image encryption using random numbers and Rivest Cipher 4 (RC4). In this paper, we perform image encryption using random numbers generated from a uniform distribution for QR codes presented at the time of payment and encrypt the seed value, which is the decryption key, using RC4. As a result, the proposed encryption method prevents unauthorized use of the QR code used for payment by stealing the image, and the processing speed and encryption strength are sufficient. Histogram analysis, key sensitivity analysis, and correlation coefficients were used to measure encryption strength. Finally, the proposed method is expected to enable more secure use of QR payments compared to conventional systems. Full article
(This article belongs to the Special Issue Computational Imaging and Its Application)
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12 pages, 4378 KiB  
Article
Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning
by Chuanchao Wu, Zhibin Wang, Peng Xue and Wenyan Liu
Electronics 2024, 13(13), 2516; https://doi.org/10.3390/electronics13132516 - 27 Jun 2024
Viewed by 598
Abstract
Microscopic and ultramicroscopic vascular sutures are indispensable in surgical procedures such as arm transplantation and finger reattachment. The state of the blood vessels after suturing, which may feature vascular patency, narrowness, and blockage, determines the success rate of the operation. If we can [...] Read more.
Microscopic and ultramicroscopic vascular sutures are indispensable in surgical procedures such as arm transplantation and finger reattachment. The state of the blood vessels after suturing, which may feature vascular patency, narrowness, and blockage, determines the success rate of the operation. If we can take advantage of the golden window of opportunity after blood vessel suture and before muscle tissue suture to achieve an accurate and objective assessment of blood vessel status, this will not only reduce medical costs but will also offer social benefits. Doppler optical coherence tomography enables the high-speed, high-resolution imaging of biological tissues, especially microscopic and ultramicroscopic blood vessels. By using Doppler optical coherence tomography to image the sutured blood vessels, a three-dimensional structure of the blood vessels and blood flow information can be obtained. By extracting the contour of the blood vessel wall and the contour of the blood flow area, the three-dimensional shape of the blood vessel can be reconstructed in three dimensions, providing parameter support for the assessment of blood vessel status. In this work, we propose a neural network-based multi-classification deep learning model that can automatically and simultaneously extract blood vessel boundaries from Doppler OCT vessel intensity images and the contours of blood flow regions from corresponding Doppler OCT vessel phase images. Compared to the traditional random walk segmentation algorithm and cascade neural network method, the proposed model can produce the vessel boundary from the intensity image and the lumen area boundary from the corresponding phase image simultaneously, achieving an average testing segmentation accuracy of 0.967 and taking, on average, 0.63 s. This method can realize system integration more easily and has great potential for clinical evaluations. It is expected to be applied to the evaluation of microscopic and ultramicroscopic vascular status in microvascular anastomosis. Full article
(This article belongs to the Special Issue Computational Imaging and Its Application)
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20 pages, 21056 KiB  
Article
Outlier Detection by Energy Minimization in Quantized Residual Preference Space for Geometric Model Fitting
by Yun Zhang, Bin Yang, Xi Zhao, Shiqian Wu, Bin Luo and Liangpei Zhang
Electronics 2024, 13(11), 2101; https://doi.org/10.3390/electronics13112101 - 28 May 2024
Viewed by 698
Abstract
Outliers significantly impact the accuracy of geometric model fitting. Previous approaches to handling outliers have involved threshold selection and scale estimation. However, many scale estimators assume that the inlier distribution follows a Gaussian model, which often does not accurately represent cases in geometric [...] Read more.
Outliers significantly impact the accuracy of geometric model fitting. Previous approaches to handling outliers have involved threshold selection and scale estimation. However, many scale estimators assume that the inlier distribution follows a Gaussian model, which often does not accurately represent cases in geometric model fitting. Outliers, defined as points with large residuals to all true models, exhibit similar characteristics to high values in quantized residual preferences, thus causing outliers to cluster away from inliers in quantized residual preference space. In this paper, we leverage this consensus among outliers in quantized residual preference space by extending energy minimization to combine model error and spatial smoothness for outlier detection. The outlier detection process based on energy minimization follows an alternate sampling and labeling framework. Subsequently, an ordinary energy minimization method is employed to optimize inlier labels, thereby following the alternate sampling and labeling framework. Experimental results demonstrate that the energy minimization-based outlier detection method effectively identifies most outliers in the data. Additionally, the proposed energy minimization-based inlier segmentation accurately segments inliers into different models. Overall, the performance of the proposed method surpasses that of most state-of-the-art methods. Full article
(This article belongs to the Special Issue Computational Imaging and Its Application)
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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 651
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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12 pages, 4458 KiB  
Article
The Design and Application of a Polarization 3D Imager for Land Object Imaging
by Yue Zhang, Jianchao Jiao, Xuemin Zhang, Yi Liu, Xuan Li and Yun Su
Electronics 2024, 13(1), 168; https://doi.org/10.3390/electronics13010168 - 30 Dec 2023
Cited by 1 | Viewed by 885
Abstract
Polarization 3D imaging is a passive, monocular, long-distance 3D imaging technology. Compared with traditional 3D imaging methods, it has many advantages, such as its lack of need for a light source, lack of need for image matching, and ability to achieve 3D imaging [...] Read more.
Polarization 3D imaging is a passive, monocular, long-distance 3D imaging technology. Compared with traditional 3D imaging methods, it has many advantages, such as its lack of need for a light source, lack of need for image matching, and ability to achieve 3D imaging using only a single image. In this study, the principle of polarization 3D imaging was introduced. In the design process of a polarization 3D imager, the acquisition method for obtaining polarization information, the extinction ratio, the spatial resolution, and the refractive index of objects was introduced in detail. The influence of these key factors on the accuracy of polarization 3D imaging was analyzed. Taking the limitations of a small satellite payload into account, specific indicators such as multi-aperture polarized imaging, a 10,000:1 extinction ratio, and a spatial resolution of 30 m were designed. The implementation and functions of the polarization 3D imager were elaborated upon, and optical systems and polarizing devices were developed. Finally, by utilizing the image data obtained by the polarization 3D imager, polarization 3D imaging of real ground objects was obtained. The accuracy of the polarization 3D imaging inversion was approximately twice the spatial resolution. These research results lay the technical foundations for the development and practical application of polarization 3D imaging technology and instruments. 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 - 9 Oct 2023
Viewed by 1197
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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