AnyMEF: Unified Multi-Exposure Fusion with a Flexible Generative Model

Multi-exposure fusion (MEF) combines images captured at different exposure levels for HDR imaging. However, existing MEF methods are typically tailored to specific exposure protocols, limiting their applicability in general scenarios. We formulate MEF as a unified observation-guided enhancement task and present AnyMEF, a generative framework that flexibly supports diverse MEF protocols. Built upon a pretrained generative model with multi-reference capabilities, AnyMEF introduces an extracted-attention-guided fusion mechanism (EAGF) to inject structural information from an arbitrary number of input images. Its synthetic training-data pipeline removes the assumption that the anchor must have a particular exposure, allowing any input frame to be selected as the anchor. In addition, AnyMEF allows adaptively routing image subareas to the transformer backbone, and implements a quality-based token selection strategy to improve efficiency. Experiments on multiple MEF benchmarks show that AnyMEF significantly outperforms existing methods while requiring only 20% as many TFLOPs and 6% as much inference time as UltraFusion.