LIIFusion: Coarse-to-fine Framework for Generative MEF via Implicit Neural Representation

Multi-exposure fusion (MEF) expands the luminance rangebeyond what a single exposure can capture. Combining images takenat different exposure levels requires handling geometric differences whilenaturally merging their complementary brightness information. It of-ten demands generative completion where details are missing. Diffusion-based generative methods address these challenges, however, they arecomputationally expensive and struggle to preserve fine structures in sat-urated regions. We propose LIIFusion, a coarse-to-fine framework thatbalances fusion quality and efficiency in generative MEF. The coarsestage performs low resolution generative fusion, enhanced by an adaptiveexposure correction that recovers structure lost in saturated over-exposedareas. The fine stage adapts a local implicit image function into a multi-exposure fusion function: conditioned on the HR OE/UE sources and thecoarse output, it queries arbitrary target coordinates and fuses sourceevidence regard- less of the HR input resolution. LIIFusion achieves upto 3.5× speed-up over existing generative methods while maintainingor improving structural fidelity and perceptual quality. We believe thisframework provides an effective pathway toward making generative MEFmore practical in real-world applications.