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A Cascaded Enhancement-Fusion Network for Visible-Infrared Imaging in Darkness
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A Cascaded Enhancement-Fusion Network for Visible-Infrared Imaging in Darkness
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A Cascaded Enhancement-Fusion Network for Visible-Infrared Imaging in Darkness
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Journal Article
A Cascaded Enhancement-Fusion Network for Visible-Infrared Imaging in Darkness
2025
Overview
This paper presents a cascaded imaging method that combines low-light enhancement and visible–long-wavelength infrared (VIS-LWIR) image fusion to mitigate image degradation in dark environments. The framework incorporates a Low-Light Enhancer Network (LLENet) for improving visible image illumination and a heterogeneous information fusion subnetwork (IXNet) for integrating features from enhanced VIS and LWIR images. Using a joint training strategy with a customized loss function, the approach effectively preserves salient targets and texture details. Experimental results on the LLVIP, M3FD, TNO, and MSRS datasets demonstrate that the method produces high-quality fused images with superior performance evaluated by quantitative metrics. It also exhibits excellent generalization ability, maintains a compact model size with low computational complexity, and significantly enhances performance in high-level visual tasks like object detection, particularly in challenging low-light scenarios.
