  Image smoothing is a fundamental low-level vision task that aims to preserve
salient structures of an image while removing insignificant details. Deep
learning has been explored in image smoothing to deal with the complex
entanglement of semantic structures and trivial details. However, current
methods neglect two important facts in smoothing: 1) naive pixel-level
regression supervised by the limited number of high-quality smoothing
ground-truth could lead to domain shift and cause generalization problems
towards real-world images; 2) texture appearance is closely related to object
semantics, so that image smoothing requires awareness of semantic difference to
apply adaptive smoothing strengths. To address these issues, we propose a novel
Contrastive Semantic-Guided Image Smoothing Network (CSGIS-Net) that combines
both contrastive prior and semantic prior to facilitate robust image smoothing.
The supervision signal is augmented by leveraging undesired smoothing effects
as negative teachers, and by incorporating segmentation tasks to encourage
semantic distinctiveness. To realize the proposed network, we also enrich the
original VOC dataset with texture enhancement and smoothing labels, namely
VOC-smooth, which first bridges image smoothing and semantic segmentation.
Extensive experiments demonstrate that the proposed CSGIS-Net outperforms
state-of-the-art algorithms by a large margin. Code and dataset are available
at https://github.com/wangjie6866/CSGIS-Net.
