Reflection Separation from a Single Image via Joint Latent Diffusion
Quick Answer
This paper introduces a novel diffusion model for single-image reflection separation, achieving superior results in glare and weak-reflection scenarios.
Quick Take
By employing a cross-layer self-attention mechanism and a disjoint sampling strategy, the method effectively disentangles transmission and reflection layers, outperforming state-of-the-art techniques on various real-world benchmarks.
Key Points
- Introduces a diffusion model fine-tuned for reflection separation from single images.
- Utilizes a cross-layer self-attention mechanism for improved feature disentanglement.
- Implements a disjoint sampling strategy to minimize layer interference during diffusion.
- Outperforms existing methods on multiple real-world benchmarks.
- Demonstrates effectiveness in handling glare and weak-reflection conditions.
Paper Resources
Source Excerpt
From the original publisher, up to about 700 charactersarXiv:2606. 04107v1 Announce Type: new Abstract: 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. …
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