Infrared–Visible Image Fusion for Enhanced Visual Carrier-Landing Perception
- 1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Abstract
Visual perception for autonomous carrier landing requires high stability and environmental adaptability. Under complex maritime conditions, such as nighttime, sea fog, and low visibility, visible images are easily affected by illumination changes and atmospheric scattering, resulting in reduced target contrast and weakened scene structures. Infrared images can provide relatively stable target information through thermal radiation differences between targets and backgrounds, but offer limited texture and fine-grained structural details. To address the difficulty of simultaneously preserving target saliency and scene structural integrity with a single modality, we propose CLSC-Fusion, an infrared–visible image fusion method for enhanced visual carrier-landing perception. Based on FusionGAN, CLSC-Fusion introduces an infrared gradient loss and a visible intensity loss to enhance the preservation of infrared salient information and visible details. An eight-neighborhood Laplacian operator with diagonal neighborhoods is further introduced into the gradient constraint to strengthen responses to multi-directional edges and texture variations. Meanwhile, the generator architecture is improved to enhance the representation of fusion features. Experiments on the TNO dataset show that CLSC-Fusion achieves competitive results in Spatial Frequency, Average Gradient, edge preservation, and PSNR, while maintaining high levels of Standard Deviation and SSIM. Ablation studies further verify the effectiveness of the proposed improvements. Case studies in visual carrier-landing scenarios demonstrate that CLSC-Fusion effectively integrates infrared target responses with structural information from flight decks, islands, and sea–sky backgrounds, providing more complete visual information for carrier-based aircraft perception under complex environments.
Related
- IV-PAFNet: Infrared-visible cross-modal alignment and fusion network for nighttime UAV object detection
- PLADOS: A Parameter-free Landing Algorithm for Decentralized Optimization on the Stiefel Manifold
- Condensed PIPG Sequential Convex Optimization for Reusable-Rocket Powered Landing with Strong Aerodynamics
- Safe Position and Attitude Control for Landing on Small Celestial Bodies with Gravitational Uncertainty
- Retraction-Free Nonsmooth Optimization on the Stiefel Manifold: Subgradient and Proximal Landing Methods
BibTeX
@article{han2026infraredvisible,
title = {{Infrared–Visible Image Fusion for Enhanced Visual Carrier-Landing Perception}},
author = {Shuai Han and Tianrui Zhao and Qidan Zhu},
journal = {Mathematics},
year = {2026},
month = nov,
doi = {10.3390/math14214033},
url = {https://www.mdpi.com/2227-7390/14/21/4033},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Infrared–Visible Image Fusion for Enhanced Visual Carrier-Landing Perception
AU - Shuai Han
AU - Tianrui Zhao
AU - Qidan Zhu
PY - 2026
DA - 2026/11/04
JO - Mathematics
DO - 10.3390/math14214033
UR - https://www.mdpi.com/2227-7390/14/21/4033
AB - Visual perception for autonomous carrier landing requires high stability and environmental adaptability. Under complex maritime conditions, such as nighttime, sea fog, and low visibility, visible images are easily affected by illumination changes and atmospheric scattering, resulting in reduced target contrast and weakened scene structures. Infrared images can provide relatively stable target information through thermal radiation differences between targets and backgrounds, but offer limited texture and fine-grained structural details. To address the difficulty of simultaneously preserving target saliency and scene structural integrity with a single modality, we propose CLSC-Fusion, an infrared–visible image fusion method for enhanced visual carrier-landing perception. Based on FusionGAN, CLSC-Fusion introduces an infrared gradient loss and a visible intensity loss to enhance the preservation of infrared salient information and visible details. An eight-neighborhood Laplacian operator with diagonal neighborhoods is further introduced into the gradient constraint to strengthen responses to multi-directional edges and texture variations. Meanwhile, the generator architecture is improved to enhance the representation of fusion features. Experiments on the TNO dataset show that CLSC-Fusion achieves competitive results in Spatial Frequency, Average Gradient, edge preservation, and PSNR, while maintaining high levels of Standard Deviation and SSIM. Ablation studies further verify the effectiveness of the proposed improvements. Case studies in visual carrier-landing scenarios demonstrate that CLSC-Fusion effectively integrates infrared target responses with structural information from flight decks, islands, and sea–sky backgrounds, providing more complete visual information for carrier-based aircraft perception under complex environments.
SN - 2227-7390
ER -