Seq-IF: Sequentially Consistent Infrared-Visible Video Fusion Under Time-Varying Illumination for Perception Enhancement

Infrared–visible image fusion leverages the complementary strengths of both modalities to enhance visual perception in challenging environments. While image-level fusion has achieved promising results, extending it to video remains challenging due to temporal illumination variations, brightness flickering, and visual inconsistency caused by motion under non-uniform illumination. To address these issues, we propose Seq-IF, a sequential fusion framework that ensures visual consistency and structural fidelity when processing video sequences. The framework comprises a static–dynamic decoupling module for robust foreground–background separation. For the background, fusion is performed by selecting the frame with the highest contrast to ensure clarity and stability. For the dynamic objects, the pixel intensity is adaptively adjusted across consecutive frames by a lightweight MLP-based illumination-consistency fine-tuning module that performs online adaptation and dynamically optimizes brightness in response to scene changes. Later we introduce a spatial–frequency fusion module integrating multi-scale encoder and edge-guided decoder to ensure structural consistency. Extensive experiments demonstrate that the fusion results produced by Seq-IF outperform baseline methods in terms of clarity, detail preservation, and illumination stability, achieving smoother temporal transitions and enhanced perceptual quality. Furthermore, the effectiveness of Seq-IF is validated on downstream tasks, including object detection and optical flow estimation, highlighting its applicability to real-world scenarios.