Deep Image Smoothing based on Texture and Structure Guidance
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Image smoothing is a fundamental task in computer vision, which aims to retain salient structures and remove insignificant textures. In this paper, we tackle the natural deficiency of existing methods, that they cannot properly distinguish textures and structures with similar low-level appearance. While deep learning approaches have addressed preserving structures, they do not yet properly address textures. To this end, we build a texture prediction network (TPN) that learns from a various of natural textures. We then combine this with a structure prediction network (SPN) so that the final double-guided filtering network (DGFN) is informed where are the textures to remove ("texture-awareness") and where are the structures to preserve ("structure-awareness"). The proposed model is easy to implement and shows excellent performance on real images in the wild as well as our synthetic dataset.
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