标题： LUT-Fuse：通过蒸馏学习可学习查找表实现极快的红外与可见光图像融合 显示英文标题

标题： LUT-Fuse: Towards Extremely Fast Infrared and Visible Image Fusion via Distillation to Learnable Look-Up Tables

摘要： 当前红外和可见光图像融合的高级研究主要集中在提高融合性能，常常忽视了在实时融合设备上的适用性。 在本文中，我们提出了一种新方法，通过蒸馏学习专门设计用于图像融合的可学习查找表，称为LUT-Fuse。 首先，我们开发了一种利用低阶近似编码和高层联合上下文场景编码的查找表结构，这非常适合多模态融合。 此外，鉴于多模态图像融合中缺乏真实标签，我们自然提出了高效的LUT蒸馏策略，而不是传统的量化LUT方法。 通过将多模态融合网络（MM-Net）的性能整合到MM-LUT模型中，我们的方法在效率和性能方面取得了显著突破。 它通常比当前轻量级SOTA融合算法所需时间少于十分之一，确保在各种场景下具有高速操作能力，即使在低功耗移动设备上也是如此。 大量实验验证了我们融合方法的优势、可靠性和稳定性。 代码可在https://github.com/zyb5/LUT-Fuse获取。 显示英文摘要

摘要： Current advanced research on infrared and visible image fusion primarily focuses on improving fusion performance, often neglecting the applicability on real-time fusion devices. In this paper, we propose a novel approach that towards extremely fast fusion via distillation to learnable lookup tables specifically designed for image fusion, termed as LUT-Fuse. Firstly, we develop a look-up table structure that utilizing low-order approximation encoding and high-level joint contextual scene encoding, which is well-suited for multi-modal fusion. Moreover, given the lack of ground truth in multi-modal image fusion, we naturally proposed the efficient LUT distillation strategy instead of traditional quantization LUT methods. By integrating the performance of the multi-modal fusion network (MM-Net) into the MM-LUT model, our method achieves significant breakthroughs in efficiency and performance. It typically requires less than one-tenth of the time compared to the current lightweight SOTA fusion algorithms, ensuring high operational speed across various scenarios, even in low-power mobile devices. Extensive experiments validate the superiority, reliability, and stability of our fusion approach. The code is available at https://github.com/zyb5/LUT-Fuse.