Reflection Separation from a Single Image via Joint Latent Diffusion
A diffusion model fine-tuned for single-image reflection separation, jointly generating transmission and reflection layers with a cross-layer self-attention mechanism and disjoint sampling strategy, achieving state-of-the-art performance on real-world benchmarks.
[2606.04107] Reflection Separation from a Single Image via Joint Latent Diffusion
[Submitted on 2 Jun 2026]
Title:Reflection Separation from a Single Image via Joint Latent Diffusion
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Abstract:Single-image reflection separation is highly challenging under extreme conditions like glare or weak reflections. Existing methods often struggle to recover both layers in glare or weak-reflection scenarios because of insufficient information. This paper presents a diffusion model explicitly fine-tuned for this task, leveraging generative diffusion priors for robust separation. Our method simultaneously generates transmission and reflection layers through a unified diffusion model, incorporating a novel cross-layer self-attention mechanism for better feature disentanglement. We further introduce a disjoint sampling strategy to iteratively reduce interference between the layers during diffusion and a latent optimization step with a learned composition function for improved results in complex real-world scenarios. Extensive experiments demonstrate that our approach surpasses state-of-the-art methods on multiple real-world benchmarks. Project page: this https URL
Comments: CVPR 2026. Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.04107 [cs.CV]
(or arXiv:2606.04107v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.04107
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zheng-Hui Huang [view email] [v1] Tue, 2 Jun 2026 18:11:20 UTC (37,119 KB)
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