CASE-GANet : Context-aware and semantic-enhanced generative adversarial network for infrared and visible image fusion

Infrared-visible (IR-VI) image fusion attempts to combine complementary thermal saliency and rich visual detail into a single valuable representation. However, contemporary fusion algorithms frequently rely primarily on low-level feature similarity or modality-agnostic attention, which results in inadequate retention of semantic relevance and object-level importance especially in complex scenarios. This research suggests Context-Aware and Semantic-Enhanced Generative Adversarial Network (CASE-GANet) that explicitly integrates semantic knowledge into the fusion process in order to overcome these constraints. Modality-specific feature extractors are used, such as an attentional MobileNet-V2 with Coordinate Attention for visible image data to capture texture-rich spatial information and a Residual Dense Block (RDB) network for infrared images to preserve weak thermal structures. A Context Score Generator uses the high-level scene comprehension produced by a semantic segmentation branch to evaluate class-wise modality relevance. Semantically guided and spatially adaptive cross-modal feature fusion is made possible by injecting this contextual information into a unique Context-Aware Multi-Head Attention (C-MHA) module via a learnable bias map. In both qualitative and quantitative assessments, extensive experiments on the TNO and Road Scene, show that the proposed strategy outperforms the latest fusion techniques. The findings verify that integrating semantic context to attention-based fusion improves overall perceptual quality, structural fidelity, and thermal target visibility.