This research focuses on the issue of single-image reflection removal (SIRR)
in real-world conditions, examining it from two angles: the collection pipeline
of real reflection pairs and the perception of real reflection locations. We
devise an advanced reflection collection pipeline that is highly adaptable to a
wide range of real-world reflection scenarios and incurs reduced costs in
collecting large-scale aligned reflection pairs. In the process, we develop a
large-scale, high-quality reflection dataset named Reflection Removal in the
Wild (RRW). RRW contains over 14,950 high-resolution real-world reflection
pairs, a dataset forty-five times larger than its predecessors. Regarding
perception of reflection locations, we identify that numerous virtual
reflection objects visible in reflection images are not present in the
corresponding ground-truth images. This observation, drawn from the aligned
pairs, leads us to conceive the Maximum Reflection Filter (MaxRF). The MaxRF
could accurately and explicitly characterize reflection locations from pairs of
images. Building upon this, we design a reflection location-aware cascaded
framework, specifically tailored for SIRR. Powered by these innovative
techniques, our solution achieves superior performance than current leading
methods across multiple real-world benchmarks. Codes and datasets will be
publicly available.

本研究关注单幅图像反射去除（SIRR）的问题，从两个角度进行研究：真实反射对的收集流程和真实反射位置的感知。我们设计了一个适应广泛真实世界反射场景的先进反射对收集流程，并在收集大规模对齐反射对方面降低成本。在此过程中，我们创建了一个大规模、高质量的反射数据集，命名为野外反射去除（RRW）。RRW 包含超过 14,950 对高分辨率真实世界反射图像，比先前的数据集大 45 倍。在反映位置感知方面，我们发现反射图像中可见的许多虚拟反射对象在对应的标准图像中并不存在。这一观察结果启发我们构想了最大反射滤波器（MaxRF）。MaxRF 能够准确明确地表征图像对中的反射位置。在此基础上，我们设计了一种反射位置感知的级联框架，专门用于 SIRR。借助这些创新技术，我们的解决方案在多个真实世界基准测试中实现了优越的性能。代码和数据集将公开提供。