Reflection Separation from a Single Image via Joint Latent Diffusion
For computer vision researchers, this work provides a robust method for reflection separation in challenging real-world scenarios, outperforming prior approaches.
This paper tackles single-image reflection separation under extreme conditions like glare or weak reflections, where existing methods fail. The proposed diffusion model with cross-layer self-attention and disjoint sampling achieves state-of-the-art results on multiple real-world benchmarks.
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/