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Digital Image Processing and Sensing Technologies—Second Edition
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Digital Image Processing and Sensing Technologies—Second Edition

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

Deadline for manuscript submissions: 20 May 2025 | Viewed by 3288

Special Issue Editors

Dr. Baptiste Magnier

E-Mail Website
Guest Editor
EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, 30100 Ales, France
Interests: image processing; multimedia security; digital images and videos; edge detection; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Stefano Berretti

E-Mail Website
Guest Editor
Media Integration and Communication Center (MICC) & Department of Information Engineering (DINFO), University of Firenze, Via S. Marta 3, 50139 Firenze, Italy
Interests: multimedia; 3D computer vision; articifial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Jean-Baptiste Thomas

E-Mail Website
Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, 2806 Gjøvik, Norway
Interests: spectral imaging; material appearance; colour imaging; computational appearance
Special Issues, Collections and Topics in MDPI journals
Dr. Khizar Hayat

E-Mail Website
Guest Editor
College of Arts and Sciences, University of Nizwa, Nizwa 616, Oman
Interests: image processing information hiding; watermarking and steganography; data science/analytics; theoretical computer science; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue “Digital Image Processing and Sensing Technologies” (https://www.mdpi.com/journal/sensors/special_issues/1F1WGCGQW1), we are pleased to announce the next in the series, entitled “Digital Image Processing and Sensing Technologies—Second Edition”. 

Thanks to new technologies, digital images and videos form part of our daily routine, allowing for the easy capture and diffusion of visual information. Digital image processing (DIP) encompasses a broad spectrum of applications, especially manipulations of digital images in the context of computer-aided automation. The boundary between DIP and computer vision (CV) is vague and may thus encompass, in addition to core processing tasks, areas such as image understanding, feature extraction, detection, pattern recognition, object detection, and so on. Moreover, multimedia (image, video, audio, text, 3D, etc.) security, in the form of copyrighting, watermarking, and image encryption, is an important aspect of modern communication. Today, digital image/video processing quintessentially contributes to almost every field, ranging from medicine, astronomy, microscopy, and defense to biology, industry, robotics, security, remote sensing, and so on. This Special Issue aims to collect papers on state-of-the-art DIP and CV, with topics of interest including (but not limited to) the following: 

  • Image acquisition;
  • Image analysis;
  • Digital image forensics;
  • Multimedia security (image and video);
  • Digital image watermarking;
  • Machine learning in DIP;
  • Image-based data hiding;
  • Image filtering;
  • Feature extraction;
  • Edge detection;
  • Corner extraction;
  • Keypoint detection;
  • Feature descriptor;
  • Image segmentation;
  • image compression;
  • Pattern recognition.

Dr. Baptiste Magnier
Dr. Stefano Berretti
Dr. Jean-Baptiste Thomas
Dr. Khizar Hayat
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

  • image acquisition
  • image analysis
  • digital image forensics
  • multimedia security (image and video)
  • digital image watermarking
  • machine learning in DIP
  • image-based data hiding
  • image filtering
  • feature extraction
  • edge detection
  • corner extraction
  • keypoint detection
  • feature descriptor
  • image segmentation
  • image compression
  • pattern recognition

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Related Special Issue

Published Papers (3 papers)

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Research

Jump to: Review

19 pages, 11145 KiB  
Article
Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique
by Kyung-Wan Seo, Junwon Park, Sang I. Park, Jeong-Hoon Song and Young-Cheol Yoon
Sensors 2025, 25(2), 410; https://doi.org/10.3390/s25020410 - 11 Jan 2025
Viewed by 894
Abstract
Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines [...] Read more.
Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments. Full article
(This article belongs to the Special Issue Digital Image Processing and Sensing Technologies—Second Edition)
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24 pages, 11011 KiB  
Article
Assessment of the Film-Free Water Decal Method for Speckle Pattern Application in Digital Image Correlation
by Anna Camille Sanchez and Dong-Keon Kim
Sensors 2024, 24(17), 5657; https://doi.org/10.3390/s24175657 - 30 Aug 2024
Cited by 2 | Viewed by 1269
Abstract
Digital Image Correlation (DIC) often encounters challenges with variability and consistency in traditional speckle pattern application techniques, such as spray-painting, affecting measurement accuracy and reliability. This study evaluates a film-free water decal method as an alternative for applying speckle patterns in DIC. SS275 [...] Read more.
Digital Image Correlation (DIC) often encounters challenges with variability and consistency in traditional speckle pattern application techniques, such as spray-painting, affecting measurement accuracy and reliability. This study evaluates a film-free water decal method as an alternative for applying speckle patterns in DIC. SS275 structural steel specimens were prepared with speckle patterns using both the film-free water decal method and traditional spray-painting. The quality of the speckle patterns was assessed, and their effectiveness for DIC was evaluated through tensile testing and a comparison with strain gauge measurements. The film-free water decal method provided enhanced control over speckle pattern application, resulting in high-quality, consistent patterns. Strain measurements obtained using this method closely matched those from traditional methods, confirming its reliability. The film-free water decal method offers a practical and reliable alternative to spray-painting, improving the consistency and accuracy of DIC experiments, with potential applications in various engineering and scientific fields. Full article
(This article belongs to the Special Issue Digital Image Processing and Sensing Technologies—Second Edition)
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Review

Jump to: Research

30 pages, 6367 KiB  
Review
Overview of Research on Digital Image Denoising Methods
by Jing Mao, Lianming Sun, Jie Chen and Shunyuan Yu
Sensors 2025, 25(8), 2615; https://doi.org/10.3390/s25082615 - 20 Apr 2025
Viewed by 269
Abstract
During image collection, images are often polluted by noise because of imaging conditions and equipment limitations. Images are also disturbed by external noise during compression and transmission, which adversely affects consequent processing, like image segmentation, target recognition, and text detection. A two-dimensional amplitude [...] Read more.
During image collection, images are often polluted by noise because of imaging conditions and equipment limitations. Images are also disturbed by external noise during compression and transmission, which adversely affects consequent processing, like image segmentation, target recognition, and text detection. A two-dimensional amplitude image is one of the most common image categories, which is widely used in people’s daily life and work. Research on this kind of image-denoising algorithm is a hotspot in the field of image denoising. Conventional denoising methods mainly use the nonlocal self-similarity of images and sparser representatives in the converted domain for image denoising. In particular, the three-dimensional block matching filtering (BM3D) algorithm not only effectively removes the image noise but also better retains the detailed information in the image. As artificial intelligence develops, the deep learning-based image-denoising method has become an important research direction. This review provides a general overview and comparison of traditional image-denoising methods and deep neural network-based image-denoising methods. First, the essential framework of classic traditional denoising and deep neural network denoising approaches is presented, and the denoising approaches are classified and summarized. Then, existing denoising methods are compared with quantitative and qualitative analyses on a public denoising dataset. Finally, we point out some potential challenges and directions for future research in the field of image denoising. This review can help researchers clearly understand the differences between various image-denoising algorithms, which not only helps them to choose suitable algorithms or improve and innovate on this basis but also provides research ideas and directions for subsequent research in this field. Full article
(This article belongs to the Special Issue Digital Image Processing and Sensing Technologies—Second Edition)
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