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AI-Driven Video and Image Processing for Multi-Sensor Data Fusion

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 35

Editor

School of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210000, China
Interests: multi-sensor data fusion; video analysis; edge AI; intelligent perception
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI-driven video and image processing for multi-sensor data fusion is an emerging research direction that has attracted enormous attention in recent years. With the widespread deployment of multiple heterogeneous sensors (e.g., RGB cameras, LiDAR, infrared, depth sensors, radar, and hyperspectral imagers), vast amounts of visual data are being generated in applications such as autonomous driving, intelligent surveillance, robotics, smart healthcare, and remote sensing. Effectively fusing these complementary data streams can significantly improve perception accuracy, robustness, and reliability. Artificial intelligence, particularly deep learning, provides powerful tools for feature extraction, cross-modal alignment, and adaptive fusion. However, several key research challenges remain within this domain, including (but not limited to):

  • Cross-modal representation learning and feature alignment;
  • Robust fusion under sensor misalignment, noise, or missing modalities;
  • Real-time video and image fusion for edge and embedded systems;
  • Transformer-based and attention-driven fusion architectures;
  • 3D perception and object detection using fused multi-sensor data;
  • Generative models and self-supervised learning for data augmentation;
  • Federated learning and privacy-preserving multi-sensor fusion;
  • Domain adaptation and generalization for heterogeneous sensor inputs;
  • Benchmark datasets and evaluation metrics for multi-sensor fusion;
  • Applications in autonomous navigation, smart cities, and industrial IoT.

Full-length original technical articles are solicited with novel contributions within the AI-driven multi-sensor fusion domain without restriction. Tutorial and survey papers are also welcome.

Dr. Mingye Ju
Guest Editor

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Keywords

  • multi-sensor fusion
  • video processing
  • image processing
  • deep learning
  • cross-modal learning
  • attention mechanisms
  • 3D perception
  • real-time systems

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Published Papers (1 paper)

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Research

25 pages, 13524 KB  
Article
Remote Sensing Image Dehazing via RGB-Space Physical Constraints
by Minxian Shen, Xucong Jiang, Chenyang Shao, Houzheng Zhang and Mingye Ju
Sensors 2026, 26(13), 4026; https://doi.org/10.3390/s26134026 (registering DOI) - 25 Jun 2026
Abstract
Haze commonly degrades visible-spectrum remote sensing (RS) images by reducing contrast and distorting colors. Existing RS dehazing methods still face two limitations. Prior-driven methods rely on handcrafted assumptions that may become unreliable in complex wide-area scenes without explicit sky regions. Learning-based methods require [...] Read more.
Haze commonly degrades visible-spectrum remote sensing (RS) images by reducing contrast and distorting colors. Existing RS dehazing methods still face two limitations. Prior-driven methods rely on handcrafted assumptions that may become unreliable in complex wide-area scenes without explicit sky regions. Learning-based methods require paired training data, yet real aligned hazy/haze-free RS image pairs are difficult to collect, which limits their real-world generalization. To address these limitations, we propose a method called Remote Sensing Image Dehazing via RGB-Space Physical Constraints (RDPC). The new method revisits the atmospheric scattering model (ASM) from the perspective of RS imaging and builds the restoration process on several physical properties of hazy image formation. For atmospheric light estimation, the RGB-space line-convergence behavior of local regions with similar reflectance and slight depth variations is exploited, allowing atmospheric light to be estimated without explicit sky areas. For transmission estimation, the geometric relation between observed pixels and atmospheric light is used in RGB space, where local perpendicularity provides physically plausible haze-removal guidance and global compensation helps avoid excessive darkening and color degradation. The estimated transmission and albedo guidance are further refined by enforcing ASM consistency and variation sparsity through joint optimization. Experiments on synthetic and real-world RS image dehazing benchmarks demonstrate that RDPC achieves competitive performance against representative prior-based and learning-based methods, including Image Dehazing and Exposure (IDE), Iterative Predictor-Critic (IPC), Curvature-to-Plane Prior (C2P), Adaptive Structure-Texture Awareness (ASTA), Asymmetric U-Net (AU-Net), Efficient Multi-scale Prior Fusion (EMPF), and Lightweight Feature Dehazing (LFD), in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), neural image assessment (NIMA), and processing time. Full article
(This article belongs to the Special Issue AI-Driven Video and Image Processing for Multi-Sensor Data Fusion)
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