FUMO: Prior-Modulated Diffusion for Single Image Reflection Removal

Single image reflection removal (SIRR) is challenging in realscenes, where reflection strength varies spatially and reflection patternsare tightly entangled with transmission structures. This paper presentsa diffusion model with prior modulation framework (FUMO) that in-troduces explicit priors for spatially adaptive conditioning and struc-turally faithful restoration. Two priors are extracted directly from themixed image, an intensity prior that estimates spatial reflection sever-ity and a high-frequency prior that captures detail-sensitive responsesvia multi-scale residual aggregation. We propose a coarse-to-fine train-ing paradigm. In the first stage, these cues are combined to gate theconditional residual injections, focusing the conditioning on regions thatare both reflection-dominant and structure-sensitive. In the second stage,a fine-grained refinement network corrects local misalignment and sharp-ens fine details in the image space. Experiments conducted on both stan-dard benchmarks and challenging images in the wild demonstrate com-petitive quantitative results and consistently improved perceptual qual-ity. The code is released at https://github.com/Lucious-Desmon/FUMO.