Abstract: Infrared and visible image fusion aims to integrate complementary information from both modalities to produce a fused image with rich texture details suitable for high-level vision tasks.However,modality discrepancies and environmental factors often lead to texture loss and reduced contrast,hindering downstream performance.To address this issue,this paper proposes an adaptive fusion network that jointly performs image fusion and semantic segmentation,incorporating dual feature compensation and illumination enhancement.Specifically,a dual feature compensation module based on the state space model(Mamba) is designed in the feature extraction stage to enhance cross-modal information interaction.Then,a recurrent neural network(RNN) is used to dynamically generate fusion weights for infrared and visible features according to their directional contributions,enabling adaptive fusion.Additionally,a front-end illumination enhancement module improves visible image quality to enrich feature representation.During training,an alternating training strategy is adopted for the fusion and segmentation tasks to achieve joint optimization. Fusion performance is evaluated on the MSRS,TNO,and RoadScene datasets,while segmentation performance is assessed on MSRS.Results demonstrate that the proposed model outperforms mainstream infrared and visible image fusion methods in both subjective quality and objective metrics.

Key words: Dual feature compensation, Adaptive fusion, Infrared and visible image fusion, Semantic segmentation, Illumination theory