用扩散模型从单张图分离反射层与透射层，应对强光弱反射难题。
Reflection Separation from a Single Image via Joint Latent Diffusion
- 用统一扩散模型联合生成反射和透射层，引入跨层自注意力提升特征解耦。
- 通过非重叠采样策略减少层间干扰，结合学习的合成函数优化结果。
- 在真实场景数据集上超越现有方法，适合图像去反射与增强任务。
单图反射分离在强光或弱反射等极端条件下极具挑战性，现有方法因信息不足难以同时恢复两层。本文提出一种专为该任务微调的扩散模型，利用生成式扩散先验实现鲁棒分离。方法通过统一扩散模型同步生成透射层与反射层，引入新颖的跨层自注意力机制以增强特征解耦。进一步设计非重叠采样策略，在扩散过程中迭代降低层间干扰，并结合学习的合成函数进行潜在空间优化，显著提升复杂真实场景下的性能。大量实验表明，本方法在多个真实世界基准上优于现有最先进方法。
原文摘要 · Abstract (English)
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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