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Applied Sciences
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Advanced Image Analysis and Processing Technologies and Applications
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Advanced Image Analysis and Processing Technologies and Applications

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 June 2024 | Viewed by 1992

Special Issue Editors

Dr. Guoxia Xu

E-Mail Website
Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjovik, Norway
Interests: pattern recognition; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hu Zhu

E-Mail Website
Guest Editor
School of Communication and Information Engineering, Nanjing University of Post and Tele Communications, Nanjing, China
Interests: tensor analysis; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image analysis and processing have far-reaching applications, and data-driven technologies have sought to explore their use across various domains. From medical imaging for disease diagnosis to autonomous vehicles relying on vision systems, we have entered a new era where digital images play a pivotal role in diverse domains. This also poses new challenges due to the limited amount of data in some real cases where there is a scarcity of training data available. Therefore, this Special Issue aims to present new ideas and experimental results on pattern recognition, machine learning, computer vision, and image analysis (from methodology, technology, and theory to its practical use).

This Special Issue aims to collect the latest research findings and achievements in the field of advanced image analysis and processing technologies and applications driven by data. Papers can focus on radar, spectral, infrared, visible light, and various imaging sensor signals.  

Potential topics include, but are not limited to, the following:

  • computational imaging technology and applications;
  • bio-medical image processing and applications;
  • aerial image processing and applications;
  • hyperspecial and multispecial image processing and applications;
  • infrared and visible image processing and applications;
  • model-driven methods in image processing;
  • data-driven methods in image processing;
  • knowledge-driven methods in image processing.

Dr. Guoxia Xu
Prof. Dr. Hu Zhu
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. 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

  • deep learning
  • model-driven
  • knowledge-driven

Published Papers (3 papers)

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Research

19 pages, 16253 KiB  
Article
Advancing Biological Research: New Automated Analysis of Immunofluorescence Signals
by Francesco Salzano, Noemi Martella, Remo Pareschi and Marco Segatto
Appl. Sci. 2024, 14(7), 2809; https://doi.org/10.3390/app14072809 - 27 Mar 2024
Viewed by 445
Abstract
In recent years, optical imaging and efficient computational approaches have improved the ability to analyse and understand biological phenomena. Immunofluorescence (IF) is a widely used immunochemical technique that provides information about protein localisation and expression levels. However, the manual analysis of IF images [...] Read more.
In recent years, optical imaging and efficient computational approaches have improved the ability to analyse and understand biological phenomena. Immunofluorescence (IF) is a widely used immunochemical technique that provides information about protein localisation and expression levels. However, the manual analysis of IF images can present important limitations, such as operator workload and interpretative bias. Thus, the development of automated tools for IF signal computation is crucial. Several software programs have been proposed to address this challenge, but there is still a need for more accurate and reliable systems. In this work, we present Q-IF, a software for automatically measuring cellular IF signals with an intuitive and easy-to-use interface. We describe the software and validate its results in different biological scenarios using SH-SY5Y neuroblastoma cells, human fibroblasts, and rat brain sections. The Q-IF system automatically carries out the entire process, from IF signal quantification to statistical analysis, thus evading operator biases and speeding up the analysis workflow. Our results demonstrate the accuracy and reliability of the Q-IF system, highlighting its potential as a valuable tool for IF analysis in biological research. Full article
(This article belongs to the Special Issue Advanced Image Analysis and Processing Technologies and Applications)
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19 pages, 13613 KiB  
Article
A Super-Resolution Reconstruction Method for Infrared Polarization Images with Sparse Representation of Over-Complete Basis Sets
by Yizhe Ma, Teng Lei, Shiyong Wang, Zhengye Yang, Linhan Li, Weidong Qu and Fanming Li
Appl. Sci. 2024, 14(2), 825; https://doi.org/10.3390/app14020825 - 18 Jan 2024
Viewed by 630
Abstract
The spatial resolution of an infrared focal plane polarization detection system is limited by the structure of the detector, resulting in lower resolution than the actual array size. To overcome this limitation and improve imaging resolution, we propose an infrared polarization super-resolution reconstruction [...] Read more.
The spatial resolution of an infrared focal plane polarization detection system is limited by the structure of the detector, resulting in lower resolution than the actual array size. To overcome this limitation and improve imaging resolution, we propose an infrared polarization super-resolution reconstruction model based on sparse representation, optimized using Stokes vector images. This model forms the basis for our method aimed at achieving super-resolution reconstruction of infrared polarization images. In this method, we utilize the proposed model to initially reconstruct low-resolution images in blocks. Subsequently, we perform a division by weight, followed by iterative back projection to enhance details and achieve high-resolution reconstruction results. As a supplement, we establish a near-real-time short-wave infrared time-sharing polarization system for data collection. The dataset was acquired to gather prior knowledge of the over-complete basis set and to generate a series of simulated focal plane images. Simulation experimental results demonstrate the superiority of our method over several advanced methods in objective evaluation indexes, exhibiting strong noise robustness in quantitative experiments. Finally, to validate the practical application of our method, we establish a split-focal plane polarization short-wave infrared system for scene testing. Experimental results confirm the effective processing of actual captured data by our method. Full article
(This article belongs to the Special Issue Advanced Image Analysis and Processing Technologies and Applications)
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19 pages, 22358 KiB  
Article
SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images
by Dongxin Fan, Yunfei Yang, Song Feng, Wei Dai, Bo Liang and Jianping Xiong
Appl. Sci. 2024, 14(1), 7; https://doi.org/10.3390/app14010007 - 19 Dec 2023
Viewed by 553
Abstract
Photospheric magnetic fields are manifested as sunspots, which cover various sizes over high-resolution, full-disk, solar continuum images. This paper proposes a novel deep learning method named SIPNet, which is designed to extract and segment multiscale sunspots. It presents a new Switchable Atrous Spatial [...] Read more.
Photospheric magnetic fields are manifested as sunspots, which cover various sizes over high-resolution, full-disk, solar continuum images. This paper proposes a novel deep learning method named SIPNet, which is designed to extract and segment multiscale sunspots. It presents a new Switchable Atrous Spatial Pyramid Pooling (SASPP) module based on ASPP, employs an IoU-aware dense object detector, and incorporates a prototype mask generation technique. Furthermore, an open-source framework known as Slicing Aided Hyper Inference (SAHI) is integrated on top of the trained SIPNet model. A comprehensive sunspot dataset is built, containing more than 27,000 sunspots. The precision, recall, and average precision metrics of the SIPNet & SAHI method were measured as 95.7%, 90.2%, and 96.1%, respectively. The results indicate that the SIPNet & SAHI method has good performance in detecting and segmenting large-scale sunspots, particularly in small and ultra-small sunspots. The method also provides a new solution for solving similar problems. Full article
(This article belongs to the Special Issue Advanced Image Analysis and Processing Technologies and Applications)
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