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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes.

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Bibliographic Details
Title: SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes.
Authors: Chen, Jun1 chenjun71983@163.com, Yang, Liling1, Yu, Wei1, Gong, Wenping2, Cai, Zhanchuan3 zccai@must.edu.mo, Ma, Jiayi4 jyma2010@gmail.com
Source: IEEE Transactions on Image Processing. 2025, Vol. 34, p3139-3153. 15p.
Subjects: Image fusion, Image processing, Digital images, Image enhancement (Imaging systems), Imaging systems
Abstract: A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. The code will be posted at: https://github.com/Liling-yang/SDSFusion. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. The code will be posted at: https://github.com/Liling-yang/SDSFusion. [ABSTRACT FROM AUTHOR]
ISSN:10577149
DOI:10.1109/TIP.2025.3571339