PRISM: Latent Composition Consistency for Single-Image Reflection Removal

Single-image reflection removal (SIRR) seeks to recover thetransmission layer from a mixture corrupted by reflections—a severelyill-posed problem. Existing methods operate in pixel space, where thenonlinear sRGB formation model entangles the two layers and limits gen-eralization. We observe that pretrained VAE latent spaces exhibit sub-stantially lower coherence between image layers compared to pixel space,providing a more favorable working space for decomposition. Buildingon this finding, we propose PRISM (Pretrained-latent Reflection ImageSeparation Model), which reinterprets SIRR as a latent linear separa-tion problem. Under an approximate additive formulation in latent space,PRISM learns a flow matching velocity field on a pretrained FLUX back-bone that recovers both transmission and reflection in a single forwardpass. To enforce robust disentanglement, we introduce a Latent Com-position Consistency (LCC) strategy that constructs synthetic mixturesby swapping reflection latents across samples and enforces consistent de-composition via a cycle loss. We further propose a Layer ContrastiveSeparation (LCS) loss that promotes semantic separation between layersthrough patch-level contrastive learning, without requiring explicit reflec-tion targets. Experiments on six benchmarks demonstrate that PRISMconsistently outperforms state-of-the-art methods by significant margins,with strong generalization to in-the-wild images.