发表机构
Yonsei University; LG Electronics（延世大学; LG电子）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对多曝光融合问题，提出LIIFusion粗到细框架，粗阶段通过自适应曝光校正进行低分辨率生成融合，细阶段将局部隐式图像函数适配为多曝光融合函数，加速达3.5倍，提升了结构保真度和感知质量，为生成式MEF实际应用提供途径。
AI 中文摘要
多曝光融合（MEF）扩展了单张曝光图像的亮度范围。结合不同曝光水平拍摄的图像需要处理几何差异并自然融合互补亮度信息，常需生成式补全缺失细节。基于扩散的生成方法虽能应对挑战，但计算昂贵且在饱和区域难以保留精细结构。我们提出LIIFusion，一种在生成式MEF中平衡融合质量与效率的粗到细框架。粗阶段进行低分辨率生成融合，通过自适应曝光校正增强，恢复饱和过曝光区域丢失的结构。细阶段将局部隐式图像函数适配为多曝光融合函数，基于高分辨率过曝光/欠曝光源和粗输出，查询任意目标坐标并融合源证据，而不考虑高分辨率输入分辨率。LIIFusion比现有生成方法加速达3.5倍，同时保持或提高结构保真度和感知质量。我们相信此框架为使生成式MEF在实际应用中更实用提供了有效途径。
英文摘要
Multi-exposure fusion (MEF) expands the luminance range beyond what a single exposure can capture. Combining images taken at different exposure levels requires handling geometric differences while naturally merging their complementary brightness information. It often demands generative completion where details are missing. Diffusion-based generative methods address these challenges, however, they are computationally expensive and struggle to preserve fine structures in saturated regions. We propose LIIFusion, a coarse-to-fine framework that balances fusion quality and efficiency in generative MEF. The coarse stage performs low resolution generative fusion, enhanced by an adaptive exposure correction that recovers structure lost in saturated over-exposed areas. The fine stage adapts a local implicit image function into a multi-exposure fusion function: conditioned on the HR OE/UE sources and the coarse output, it queries arbitrary target coordinates and fuses source evidence regardless of the HR input resolution. LIIFusion achieves up to 3.5$\times$ speed-up over existing generative methods while maintaining or improving structural fidelity and perceptual quality. We believe this framework provides an effective pathway toward making generative MEF more practical in real-world applications.
