发表机构
Foshan University; North University of China（佛山大学; 中北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对偏振图像融合中DoLP可靠性差异问题，提出轻量级置信度引导框架LG-PF，通过置信度先验、掩码多尺度融合和校正头实现选择性残差传递，在MSP数据集上六项指标最优，且参数少、推理快。
AI 中文摘要
偏振图像融合将总强度图像S0的稳定亮度和结构信息与线偏振度（DoLP）图像的材料敏感细节相结合。然而，DoLP的可靠性在空间上存在差异，不加区分的偏振传递可能会放大不稳定的响应或干扰由S0锚定的结构外观。因此，我们提出了LG-PF，一个轻量级的置信度引导框架，将偏振融合表述为选择性残差传递过程。偏振置信度先验估计空间上可靠的偏振响应，掩码引导的多尺度融合模块在三个特征尺度上调节其传递，轻量级上下文感知有界校正头稳定局部光度与结构过渡。置信度引导也被纳入优化目标中，以保留可靠的偏振细节，同时抑制不支持的响应。我们还构建了MSP，一个包含来自17个室内和室外场景类别的1000对像素对齐图像对的多场景偏振融合数据集。LG-PF在MSP上的所有六个评估指标上均取得了最佳结果，而在PIF和GAND上的基于子集的评估显示了无需微调即可获得良好的可迁移性。仅0.2936 M参数和每幅图像21.712 ms的推理时间，LG-PF以低计算成本实现了有竞争力的融合质量。源代码、数据集和官方数据划分将在发表后公开提供。
英文摘要
Polarization image fusion combines the stable luminance and structural information of the total- intensity image S0 with the material-sensitive details of the degree of linear polarization (DoLP) image. However, the reliability of DoLP varies spatially, and indiscriminate polarization transfer may amplify unstable responses or disturb the structural appearance anchored by S0. We therefore propose LG-PF, a lightweight confidence-guided framework that formulates polarization fusion as a selective residual transfer process. A Polarization Confidence Prior estimates spatially reliable polarization responses, a Mask-guided Multi-scale Fusion module regulates their transfer across three feature scales, and a Lightweight Context-aware Bounded Correction Head stabilizes local photometric and structural transitions. Confidence guidance is also incorporated into the optimization objectives to preserve reliable polarization details while suppressing unsupported responses. We also construct MSP, a multi-scene polarization fusion dataset containing 1000 pixel-aligned image pairs from 17 indoor and outdoor scene categories. LG-PF achieves the best results across all six evaluated metrics on MSP, while subset-based evaluations on PIF and GAND show promising transferability without fine-tuning. With only 0.2936 M parameters and an inference time of 21.712 ms per image, LG-PF achieves competitive fusion quality with low computational cost. The source code will be available at https://github.com/1hzf/LG-PF.
