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Image Sensors and Camera Development
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Image Sensors and Camera Development

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

Deadline for manuscript submissions: closed (20 December 2025) | Viewed by 3403

Special Issue Editor

Prof. Dr. Perry Xiao

E-Mail Website
Guest Editor
School of Engineering, London South Bank University, London SE1 0AA, UK
Interests: infrared sensing technologies; lasers and detectors; capacitive imaging; skin hydration; embedded systems; Internet of Things; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the field of image sensors and camera development has undergone unprecedented advancements, shaping the way we capture and perceive visual information. The number of publications has risen from approximately 600 in 2000 to approximately 11,000 in 2023. This Special Issue seeks to explore the cutting-edge innovations, emerging technologies, and diverse applications within this dynamic realm.

Image sensors, the backbone of modern imaging devices, have experienced significant evolution, driven by breakthroughs in semiconductor technology, miniaturization, and enhanced sensitivity. This progress has led to the development of high-resolution sensors, improved low-light performance, and novel sensor architectures. These advancements not only impact traditional photography but also play a pivotal role in various domains, including medical imaging, autonomous vehicles, surveillance systems, and augmented reality.

The applications of image sensors and camera technologies are vast and continue to expand. From enhancing healthcare diagnostics to autonomous vehicles, the impact is far-reaching. Moreover, the integration of image sensors in smartphones has democratized photography, empowering individuals to capture and share moments seamlessly.

This Special Issue provides a platform for researchers, engineers, and industry experts to share their latest findings and contributions to the field. We invite submissions that delve into topics such as sensor design, signal processing algorithms, novel imaging techniques, and real-world applications. By contributing to this Special Issue, you have the opportunity to shape the discourse on image sensors and camera development, influencing the next wave of innovations in this critical field. Submit your groundbreaking research and be a part of this transformative journey.

Prof. Dr. Perry Xiao
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 250 words) can be sent to the Editorial Office for assessment.

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 sensors
  • camera development
  • imaging devices, high-resolution sensors, medical imaging
  • autonomous vehicles, surveillance systems
  • augmented reality

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Published Papers (2 papers)

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Research

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19 pages, 6259 KB  
Article
A Yolo-Based Semantic Segmentation Model for Solar Photovoltaic Panel Identification
by Jiandong Zhang, Daqing Chen, Bo Li, Zhanfang Zhao, Huibo Bi and Perry Xiao
Sensors 2026, 26(1), 75; https://doi.org/10.3390/s26010075 - 22 Dec 2025
Viewed by 1247
Abstract
The global shift towards renewable energy is increasingly driven by the need to reduce carbon emissions and address urban energy demands sustainably. Solar power, as an accessible and efficient energy source, offers substantial potential for integration within urban environments. However, there remains a [...] Read more.
The global shift towards renewable energy is increasingly driven by the need to reduce carbon emissions and address urban energy demands sustainably. Solar power, as an accessible and efficient energy source, offers substantial potential for integration within urban environments. However, there remains a lack of a comprehensive evaluation framework for accurately predicting the energy generation of urban solar panel installations. Therefore, in this study, we develop a YOLO-based semantic segmentation framework to estimate the energy generation potential of existing solar panels in a city-scale fashion and use the Elephant andCastle area of London city as a case study. The results demonstrate that the proposed model can detect and segment solar panels in complex urban environments with an accuracy of 98.32%, and the total area of solar panels in the designated area is 127.75 m2. Full article
(This article belongs to the Special Issue Image Sensors and Camera Development)
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Review

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23 pages, 4054 KB  
Review
Recent Advances in Deep Learning-Based Source Camera Identification and Device Linking
by Zimeng Li and Ngai-Fong Law
Sensors 2025, 25(24), 7432; https://doi.org/10.3390/s25247432 - 6 Dec 2025
Cited by 1 | Viewed by 1464
Abstract
Photo-response non-uniformity (PRNU) has long been regarded as a reliable method for source camera identification and device linking in forensic applications. Recent advances in deep learning (DL) have introduced diverse architectures, including convolutional neural networks, residual learning, encoder–decoder representations, dual-branch structures, and contrastive [...] Read more.
Photo-response non-uniformity (PRNU) has long been regarded as a reliable method for source camera identification and device linking in forensic applications. Recent advances in deep learning (DL) have introduced diverse architectures, including convolutional neural networks, residual learning, encoder–decoder representations, dual-branch structures, and contrastive learning, to capture specific sensor artifacts. This review summarizes the performance of these DL techniques across both tasks and compares their effectiveness at the model and device levels over time. While DL approaches achieve strong model-level accuracy, robust device-level identification remains challenging, particularly in modern imaging pipelines that involve camera-integrated or AI-driven enhancements during capture. These findings underscore the need for improved techniques and updated datasets to address evolving photograph capture practices. Full article
(This article belongs to the Special Issue Image Sensors and Camera Development)
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