There and Back Again: A Flexible-Frame Transformer for Multi-Exposure Fusion

Multi-exposure fusion (MEF) brings the dynamic range ofconventional cameras closer to that of human vision, producing imageswith rich scene content. Given the large variability in scene luminance,exposure strategies often require different numbers of frames to capturethe full radiance range faithfully. However, conventional MEF techniquesare typically designed for a fixed number of inputs, forcing deploymentsystems to maintain separate models for different frame-count require-ments, which undermines deployment efficiency. To address this limita-tion, we propose FreeMEF, the first flexible-frame transformer for MEFthat seamlessly accommodates varying numbers of input exposures with-out retraining or architectural changes. The proposed approach consistsof two key modules. First, we introduce a recurrent state space mod-ule (RSSM) that sequentially fuses features from arbitrary sequencesvia adaptive alignment and state-space recurrent modeling, thereby pro-viding global information guidance for the subsequent restoration. Sec-ond, we devise a global feature guided block (GFGB) incorporating anextremity-aware hybrid attention (EAHA) and an affine-injection feed-forward network (AFFN), which effectively resolves the similarity para-dox while simultaneously optimizing contrast and brightness regulation.Extensive experiments on three benchmark datasets demonstrate the ef-fectiveness of our method, which performs favorably against state-of-the-art methods both quantitatively and qualitatively. The code is availableat https://github.com/qulishen/FreeMEF.