Reflection Separation from a Single Image via Joint Latent Diffusion

Single-image reflection separation remains challenging due to its ill-posed nature, especially under extreme conditions with strong or subtle reflections. Existing methods often struggle to recover both layers in glare or weak-reflection scenarios, because of insufficient information. This paper presents the first 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 show our approach achieves superior separation performance on multiple real-world benchmarks and surpasses state-of-the-art methods in both quantitative metrics and perceptual quality.