Dereflection Any Image with Diffusion Priors and Diversified Data

• URL: http://arxiv.org/abs/2503.17347v1
• Date: Fri, 21 Mar 2025 17:48:14 GMT
• Title: Dereflection Any Image with Diffusion Priors and Diversified Data
• Authors: Jichen Hu, Chen Yang, Zanwei Zhou, Jiemin Fang, Xiaokang Yang, Qi Tian, Wei Shen,
• Abstract summary: We propose a comprehensive solution with an efficient data preparation pipeline and a generalizable model for robust reflection removal.<n>First, we introduce a dataset named Diverse Reflection Removal (DRR) created by randomly rotating reflective mediums in target scenes.<n>Second, we propose a diffusion-based framework with one-step diffusion for deterministic outputs and fast inference.
• Score: 86.15504914121226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reflection removal of a single image remains a highly challenging task due to the complex entanglement between target scenes and unwanted reflections. Despite significant progress, existing methods are hindered by the scarcity of high-quality, diverse data and insufficient restoration priors, resulting in limited generalization across various real-world scenarios. In this paper, we propose Dereflection Any Image, a comprehensive solution with an efficient data preparation pipeline and a generalizable model for robust reflection removal. First, we introduce a dataset named Diverse Reflection Removal (DRR) created by randomly rotating reflective mediums in target scenes, enabling variation of reflection angles and intensities, and setting a new benchmark in scale, quality, and diversity. Second, we propose a diffusion-based framework with one-step diffusion for deterministic outputs and fast inference. To ensure stable learning, we design a three-stage progressive training strategy, including reflection-invariant finetuning to encourage consistent outputs across varying reflection patterns that characterize our dataset. Extensive experiments show that our method achieves SOTA performance on both common benchmarks and challenging in-the-wild images, showing superior generalization across diverse real-world scenes.

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