ZVEFusion: Zero-Shot Visual Enhancement Fusion for Infrared and Visible Images in Low Light

Infrared and visible image fusion (IVIF) aims to generate fused images with prominent targets and rich scene information. However, in low-light conditions, visible images lose accurate texture and color, reducing their ability to provide detailed scene information for fusion. Existing IVIF methods often overlook illumination degradation and cause color distortion when incorporating infrared information. To address these problems, we propose a novel visually enhanced IVIF method tailored for low-light environments. Our method combines low-light image enhancement (LLIE) and IVIF into a single module. First, we adaptively enhance the low-light visible image, ensuring rich texture and color for fusion. Additionally, we introduce a three-channel fusion coefficient map to transform infrared information into visible image, preventing color distortion and highlighting key targets while maintaining details of the fused image. Since infrared and visible images are from different modalities, we map them into the same high-dimensional feature space. We then propose the feature difference to integrate complementary information, producing a fused image with complete content and no redundancy. Notably, our method is zero-shot, requiring only a pair of test infrared and visible images for training. This better meets the complexity of IVIF in various low-light scenes. Extensive experiments show that in low-light conditions, our method surpasses other state-of-the-art (SOTA) methods by providing more natural colors, richer textures, and better alignment with human visual perception.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10888684
Citations
1
Primary Topic
Advanced Image Fusion Techniques
Type
article
Field-Weighted Citation Impact
1.14

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article

ZVEFusion: Zero-Shot Visual Enhancement Fusion for Infrared and Visible Images in Low Light

Sizhao Li, Yiqi Shi, Liguo Zhang, Duo Liu et al.
1 citations
Advanced Image Fusion Techniques
1.14
article

ZVEFusion: Zero-Shot Visual Enhancement Fusion for Infrared and Visible Images in Low Light

Sizhao Li, Yiqi Shi, Liguo Zhang, Duo Liu, Guoyin Zhang
article en
1 citations

Abstract

Infrared and visible image fusion (IVIF) aims to generate fused images with prominent targets and rich scene information. However, in low-light conditions, visible images lose accurate texture and color, reducing their ability to provide detailed scene information for fusion. Existing IVIF methods often overlook illumination degradation and cause color distortion when incorporating infrared information. To address these problems, we propose a novel visually enhanced IVIF method tailored for low-light environments. Our method combines low-light image enhancement (LLIE) and IVIF into a single module. First, we adaptively enhance the low-light visible image, ensuring rich texture and color for fusion. Additionally, we introduce a three-channel fusion coefficient map to transform infrared information into visible image, preventing color distortion and highlighting key targets while maintaining details of the fused image. Since infrared and visible images are from different modalities, we map them into the same high-dimensional feature space. We then propose the feature difference to integrate complementary information, producing a fused image with complete content and no redundancy. Notably, our method is zero-shot, requiring only a pair of test infrared and visible images for training. This better meets the complexity of IVIF in various low-light scenes. Extensive experiments show that in low-light conditions, our method surpasses other state-of-the-art (SOTA) methods by providing more natural colors, richer textures, and better alignment with human visual perception.

Harbin Engineering University (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 23%
Advanced Image Fusion Techniques
1.14
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ZVEFusion: Zero-Shot Visual Enhancement Fusion for Infrared and Visible Images in Low Light — Sizhao Li, Yiqi Shi, et al. · (2025) | TGRS Research Map | TGRS