A Generative Diffusion Framework for Single Image Reflection Separation

Zheng-Hui Huang, Zhixiang Wang, Yu-Lun Liu, Yung-Yu Chuang

Keywords: Reflection Separation, Diffusion Model

Abstract: 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.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 2944