Removing undesirable reflections from a single image captured through a glass
window is of practical importance to visual computing systems. Although
state-of-the-art methods can obtain decent results in certain situations,
performance declines significantly when tackling more general real-world cases.
These failures stem from the intrinsic difficulty of single image reflection
removal -- the fundamental ill-posedness of the problem, and the insufficiency
of densely-labeled training data needed for resolving this ambiguity within
learning-based neural network pipelines. In this paper, we address these issues
by exploiting targeted network enhancements and the novel use of misaligned
data. For the former, we augment a baseline network architecture by embedding
context encoding modules that are capable of leveraging high-level contextual
clues to reduce indeterminacy within areas containing strong reflections. For
the latter, we introduce an alignment-invariant loss function that facilitates
exploiting misaligned real-world training data that is much easier to collect.
Experimental results collectively show that our method outperforms the
state-of-the-art with aligned data, and that significant improvements are
possible when using additional misaligned data.

本文利用目标网络增强和不对齐数据的新颖用途，解决了单张图像反射去除的困难问题，在含有强烈反射区域的上下文编码模块和对齐不变损失函数的引入下，实现了对真实世界情况的显著改进。