Beyond Sharp Coverage: Focus-Aware Content Routing for Generalized Multi-Focus Image Fusion

Multi-focus image fusion (MFIF) combines differently focused images into an all-in-focus result. Conventional methods rely on a sharp-coverage assumption, where each scene region is sufficiently observed in at least one input. In practice, however, limited focal-plane sampling and defocus spread can leave regions blurred in every input, while small viewpoint differences further complicate transferring observed content. We formulate these challenging cases as Generalized MFIF, where source selection alone is insufficient. We then introduce two benchmarks (CommonBlurGeometry and RealScene-68) and a region-wise evaluation protocol. The proposed Focus-Aware Content Routing framework uses continuous focus estimates to route each region according to the availability of reliable sharp observations: it preserves what is reliably observed and restores only where observations are insufficient. A focus-aware router determines the spatial contribution of the source observations and a restored candidate predicted by a one-step pixel-space restorer, while a contextual refiner corrects local inconsistencies in the composite. Our method achieves 32.64 dB on CommonBlurGeometry, exceeding the strongest external baseline by 3.86 dB, leads regional evaluation on RealScene-68, and reaches 42.93 dB on RealMFF. Inference takes 0.190 s at \(625\times433\) and 2.869 s at \(4104\times2736\) per image.