Several Points Are All It Takes: Saluting User-Assisted Single Image Reflection Removal

Reflection removal is essential for applications in photography, object detection, and augmented reality. Single-image reflection removal (SIRR) offers greater flexibility and applicability than multi-image methods, making it ideal for real-time scenarios. However, strong reflections obscure large portions of the transmission layer, limiting the performance of existing methods. We propose a novel user-assisted approach for SIRR, where users annotate occluded objects by selecting their categories and locations. This provides critical semantic information to guide accurate transmission layer recovery. Additionally, we design a hybrid CNN-Transformer network that leverages local feature extraction and global context modeling to address strong reflection challenges. Experiments on the strong reflection datasets demonstrate the effectiveness of our method, achieving significant improvements in transmission layer recovery and outperforming existing advanced methods across multiple metrics.