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Removing undesirable reflections from a single image captured through a glasswindow is of practical importance to visual computing systems. Althoughstate-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 reflectionremoval -- the fundamental ill-posedness of the problem, and the insufficiencyof densely-labeled training data needed for resolving this ambiguity withinlearning-based neural network pipelines. In this paper, we address these issuesby exploiting targeted network enhancements and the novel use of misaligneddata. For the former, we augment a baseline network architecture by embeddingcontext encoding modules that are capable of leveraging high-level contextualclues to reduce indeterminacy within areas containing strong reflections. Forthe latter, we introduce an alignment-invariant loss function that facilitatesexploiting misaligned real-world training data that is much easier to collect.Experimental results collectively show that our method outperforms thestate-of-the-art with aligned data, and that significant improvements arepossible when using additional misaligned data.

Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements