# Cooperative Semantic Segmentation and Image Restoration in Adverse Environmental Conditions

arXiv: https://arxiv.org/abs/1911.00679 · HTML: https://mlsanity.com/paper/1911.00679/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [GAN Inversion: A Survey](https://mlsanity.com/paper/2101.05278/) (2021)
- [RobustSAM: Segment Anything Robustly on Degraded Images](https://mlsanity.com/paper/2406.09627/) (2024)
- [Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning](https://mlsanity.com/paper/2401.03070/) (2024)
- [Defurnishing with X-Ray Vision: Joint Removal of Furniture from Panoramas and Mesh](https://mlsanity.com/paper/2506.05338/) (2025)
- [Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation](https://mlsanity.com/paper/2509.11485/) (2025)
- [Toward Robust In-Context Segmentation via Concept Guidance](https://mlsanity.com/paper/2606.28149/) (2026)

## Abstract (arXiv)

Most state-of-the-art semantic segmentation approaches only achieve high accuracy in good conditions. In practically-common but less-discussed adverse environmental conditions, their performance can decrease enormously. Existing studies usually cast the handling of segmentation in adverse conditions as a separate post-processing step after signal restoration, making the segmentation performance largely depend on the quality of restoration. In this paper, we propose a novel deep-learning framework to tackle semantic segmentation and image restoration in adverse environmental conditions in a holistic manner. The proposed approach contains two components: Semantically-Guided Adaptation, which exploits semantic information from degraded images to refine the segmentation; and Exemplar-Guided Synthesis, which restores images from semantic label maps given degraded exemplars as the guidance. Our method cooperatively leverages the complementarity and interdependence of low-level restoration and high-level segmentation in adverse environmental conditions. Extensive experiments on various datasets demonstrate that our approach can not only improve the accuracy of semantic segmentation with degradation cues, but also boost the perceptual quality and structural similarity of image restoration with semantic guidance.