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Multisensor Image and Video Processing: Methods and Applications

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

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1542

Special Issue Editor

Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China
Interests: computer vision; object tracking; machine learning; self-supervised learning; active learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the integration and processing of multimodal data—such as RGB, thermal infrared, depth, and LiDAR—for various visual tasks, including detection, classification, segmentation, and tracking. Multisensor imaging is becoming crucial for modern visual perception systems. By combining complementary sources such as RGB, thermal infrared, depth, LiDAR, and event-based sensors, researchers and engineers can significantly enhance the understanding of scenes in challenging real-world environments.

This Special Issue seeks to compile the latest advancements in image and video processing methods based on multisensor inputs. We encourage the submission of papers that address theoretical models, algorithmic strategies, system-level solutions, and real-world applications in multisensor visual analysis.

Topics of interest include, but are not limited to, the following:

--Multi-modal image and video fusion techniques;
--Object detection, recognition, and classification using multisensor data;
--Semantic and instance segmentation from heterogeneous inputs;
--Multimodal tracking and spatiotemporal analysis;
--Feature extraction and representation learning across sensor domains;
--Cross-modal alignment, transfer learning, and domain adaptation;
--Real-time perception systems and embedded vision with sensor fusion;
--Benchmark datasets and evaluation metrics for multisensor processing;
--Applications in autonomous driving, smart surveillance, robotics, healthcare, and the IoT.

Dr. Di Yuan
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

  • multisensor fusion
  • multimodal data
  • image and video processing
  • object detection

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Published Papers (1 paper)

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Review

39 pages, 2583 KB  
Review
Efficient Medical Image Segmentation in Multisensor Imaging: A Survey in the Era of Mamba and Foundation Models
by Xiu Shu, Youqiang Xiong, Zhangli Ma, Xinming Zhang and Di Yuan
Sensors 2026, 26(8), 2558; https://doi.org/10.3390/s26082558 - 21 Apr 2026
Viewed by 1113
Abstract
Deep learning has revolutionized medical image segmentation; however, the clinical deployment of state-of-the-art models is severely impeded by their quadratic computational complexity and substantial resource demands, particularly in multisensor and multimodal imaging scenarios. In response, the field is undergoing a paradigm shift towards [...] Read more.
Deep learning has revolutionized medical image segmentation; however, the clinical deployment of state-of-the-art models is severely impeded by their quadratic computational complexity and substantial resource demands, particularly in multisensor and multimodal imaging scenarios. In response, the field is undergoing a paradigm shift towards efficiency, characterized by the rise of linear-complexity architectures and the optimization of foundation models. This paper presents a comprehensive survey of efficient medical image segmentation methodologies, systematically reviewing the evolution from heavy, accuracy-driven models to lightweight, deployment-ready paradigms. In particular, we highlight the growing importance of efficient segmentation in multisensor medical imaging, where heterogeneous data sources such as CT, MRI, ultrasound, and infrared imaging introduce additional challenges in scalability and computational cost. We propose a novel taxonomy that categorizes these advancements into four distinct streams: (1) Mamba and State Space Models, which leverage selective scanning mechanisms to achieve global receptive fields with linear complexity; (2) Efficient Adaptation of Foundation Models, focusing on parameter-efficient fine-tuning and knowledge distillation to tailor the Segment Anything Model (SAM) for medical domains; (3) Advanced Lightweight Architectures, covering the resurgence of large-kernel CNNs and the emergence of Kolmogorov–Arnold Networks (KANs); and (4) Data-Efficient Strategies, including semi-supervised and federated learning to address annotation scarcity. Furthermore, we conduct a rigorous comparative analysis of representative algorithms on mainstream benchmarks, providing a granular evaluation of the trade-offs between segmentation accuracy and computational overhead. The survey also discusses key challenges in multisensor and multimodal settings, including modality heterogeneity, data fusion complexity, and resource constraints. Finally, we identify critical challenges and outline future research directions, serving as a roadmap for the development of next-generation efficient and scalable medical image analysis systems. Full article
(This article belongs to the Special Issue Multisensor Image and Video Processing: Methods and Applications)
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