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Most state-of-the-art semantic segmentation approaches only achieve highaccuracy in good conditions. In practically-common but less-discussed adverseenvironmental conditions, their performance can decrease enormously. Existingstudies usually cast the handling of segmentation in adverse conditions as aseparate post-processing step after signal restoration, making the segmentationperformance largely depend on the quality of restoration. In this paper, wepropose a novel deep-learning framework to tackle semantic segmentation andimage restoration in adverse environmental conditions in a holistic manner. Theproposed approach contains two components: Semantically-Guided Adaptation,which exploits semantic information from degraded images to refine thesegmentation; and Exemplar-Guided Synthesis, which restores images fromsemantic label maps given degraded exemplars as the guidance. Our methodcooperatively leverages the complementarity and interdependence of low-levelrestoration and high-level segmentation in adverse environmental conditions.Extensive experiments on various datasets demonstrate that our approach can notonly improve the accuracy of semantic segmentation with degradation cues, butalso boost the perceptual quality and structural similarity of imagerestoration with semantic guidance.

Cooperative Semantic Segmentation and Image Restoration in Adverse Environmental Conditions