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arXiv · 2606.04107

Reflection Separation from a Single Image via Joint Latent Diffusion

Zheng-Hui Huang·Zhixiang Wang·Yu-Lun Liu·Yung-Yu Chuang
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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: https://brian90709.github.io/diff-reflection-separation/

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Zheng-Hui Huang, Zhixiang Wang, Yu-Lun Liu, Yung-Yu Chuang. 2026-06-02. Reflection Separation from a Single Image via Joint Latent Diffusion. https://arxiv.org/abs/2606.04107

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