  Image smoothing is a fundamental task in computer vision, that aims to retain
salient structures and remove insignificant textures. In this paper, we aim to
address the fundamental shortcomings of existing image smoothing methods, which
cannot properly distinguish textures and structures with similar low-level
appearance. While deep learning approaches have started to explore the
preservation of structure through image smoothing, existing work does not yet
properly address textures. To this end, we generate a large dataset by blending
natural textures with clean structure-only images, and then build a texture
prediction network (TPN) that predicts the location and magnitude of textures.
We then combine the TPN with a semantic structure prediction network (SPN) so
that the final texture and structure aware filtering network (TSAFN) is able to
identify the textures to remove ("texture-awareness") and the structures to
preserve ("structure-awareness"). The proposed model is easy to understand and
implement, and shows excellent performance on real images in the wild as well
as our generated dataset.
