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This paper proposes a deep neural network structure that exploits edgeinformation in addressing representative low-level vision tasks such as layerseparation and image filtering. Unlike most other deep learning strategiesapplied in this context, our approach tackles these challenging problems byestimating edges and reconstructing images using only cascaded convolutionallayers arranged such that no handcrafted or application-specificimage-processing components are required. We apply the resulting transferrablepipeline to two different problem domains that are both sensitive to edges,namely, single image reflection removal and image smoothing. For the former,using a mild reflection smoothness assumption and a novel synthetic datageneration method that acts as a type of weak supervision, our network is ableto solve much more difficult reflection cases that cannot be handled byprevious methods. For the latter, we also exceed the state-of-the-artquantitative and qualitative results by wide margins. In all cases, theproposed framework is simple, fast, and easy to transfer across disparatedomains.

A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing