Adaptive Language-Aware Image Reflection Removal Network
Existing image reflection removal methods struggle to handle complex reflections. Accurate language descriptions can help the model understand the image content to remove complex reflections. However, due to blurred and distorted interferences in reflected images, machine-generated language descriptions of the image content are often inaccurate, which harms the performance of language-guided reflection removal. To address this, we propose the Adaptive Language-Aware Network (ALANet) to remove reflections even with inaccurate language inputs. Specifically, ALANet integrates both filtering and optimization strategies. The filtering strategy reduces the negative effects of language while pre...
| Authors | Siyan Fang, Yuntao Wang, Jinpu Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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