Rectifying Latent Space for Generative Single-Image Reflection Removal

• URL: http://arxiv.org/abs/2512.06358v1
• Date: Sat, 06 Dec 2025 09:16:14 GMT
• Title: Rectifying Latent Space for Generative Single-Image Reflection Removal
• Authors: Mingjia Li, Jin Hu, Hainuo Wang, Qiming Hu, Jiarui Wang, Xiaojie Guo,
• Abstract summary: Single-image removal is a highly ill-posed problem, where existing methods struggle to reason about the composition of corrupted regions.<n>This work reframes an editing-purpose latent diffusion model to effectively perceive and process highly ambiguous, layered image inputs.
• Score: 16.341477336909765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single-image reflection removal is a highly ill-posed problem, where existing methods struggle to reason about the composition of corrupted regions, causing them to fail at recovery and generalization in the wild. This work reframes an editing-purpose latent diffusion model to effectively perceive and process highly ambiguous, layered image inputs, yielding high-quality outputs. We argue that the challenge of this conversion stems from a critical yet overlooked issue, i.e., the latent space of semantic encoders lacks the inherent structure to interpret a composite image as a linear superposition of its constituent layers. Our approach is built on three synergistic components, including a reflection-equivariant VAE that aligns the latent space with the linear physics of reflection formation, a learnable task-specific text embedding for precise guidance that bypasses ambiguous language, and a depth-guided early-branching sampling strategy to harness generative stochasticity for promising results. Extensive experiments reveal that our model achieves new SOTA performance on multiple benchmarks and generalizes well to challenging real-world cases.

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