FIRM：灵活交互式反射去除
FIRM: Flexible Interactive Reflection reMoval
AI总结：
本文提出FIRM框架，通过统一对比掩码和交叉注意力机制，支持稀疏视觉或文本引导，仅需先前方法10%的引导时间，实现最先进的单图反射去除。
摘要：
由于缺乏通用的反射先验，从单张图像中去除反射具有挑战性。尽管现有方法通过大量用户引导获得了令人满意的性能，但它们往往缺乏在不同模态下调整用户引导的灵活性，且密集的用户交互进一步限制了其实用性。为缓解这些问题，本文提出了FIRM，一种新颖的灵活交互式图像反射去除框架，支持多种形式的引导，用户可以提供稀疏的视觉引导（如点、框或笔画）或文本描述以实现更好的反射去除。首先，我们设计了一个新颖的用户引导转换模块（UGC），将不同形式的引导转换为统一的对比掩码。对比掩码为识别混合图像中的反射层和透射层提供了明确的线索。其次，我们设计了一个对比掩码引导的反射去除网络，其中包含一个新提出的对比引导交互块（CGIB）。该块利用独特的交叉注意力机制，将对比掩码与图像特征融合，实现精确的层分离。所提出的框架仅需先前交互式方法所需引导时间的10%，在灵活性上实现了阶跃式提升。在公开的真实世界反射去除数据集上的大量实验结果表明，我们的方法达到了最先进的反射去除性能。代码可在 https://github.com/ShawnChenn/FlexibleReflectionRemoval 获取。
英文摘要：
Removing reflection from a single image is challenging due to the absence of general reflection priors. Although existing methods incorporate extensive user guidance for satisfactory performance, they often lack the flexibility to adapt user guidance in different modalities, and dense user interactions further limit their practicality. To alleviate these problems, this paper presents FIRM, a novel framework for Flexible Interactive image Reflection reMoval with various forms of guidance, where users can provide sparse visual guidance (e.g., points, boxes, or strokes) or text descriptions for better reflection removal. Firstly, we design a novel user guidance conversion module (UGC) to transform different forms of guidance into unified contrastive masks. The contrastive masks provide explicit cues for identifying reflection and transmission layers in blended images. Secondly, we devise a contrastive mask-guided reflection removal network that comprises a newly proposed contrastive guidance interaction block (CGIB). This block leverages a unique cross-attention mechanism that merges contrastive masks with image features, allowing for precise layer separation. The proposed framework requires only 10\% of the guidance time needed by previous interactive methods, which makes a step-change in flexibility. Extensive results on public real-world reflection removal datasets validate that our method demonstrates state-of-the-art reflection removal performance. Code is avaliable at https://github.com/ShawnChenn/FlexibleReflectionRemoval.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
