Single Image Reflection Removal Using Non-Linearly Synthesized Glass Images and Semantic Context

Abstract

An image captured through a glass plane usually contains both of a target transmitted scene behind the glass plane and a reflected scene in front of the glass plane. We propose a semantic context based network to remove reflection artifacts from a single glass image. We first investigate a non-linear intensity mapping relationship for glass images to synthesize more realistic training sets. Then we devise an efficient reflection removal network using multi-scale generators and an interpreter, where the semantic context of the transmission image is adopted as a high level cue for the interpreter to guide the generators. We also provide a new test data set of real glass images including the ground truth transmission and reflection images. Experiments are performed on four test data sets and we show that the proposed algorithm decomposes an input glass image into a transmission image and a reflection image more faithfully compared with the four existing state-of-the-art methods.