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[2602.14042] Restoration Adaptation for Semantic Segmentation on Low Quality Images
Summary
This paper presents a novel approach, Restoration Adaptation for Semantic Segmentation (RASS), which enhances semantic segmentation performance on low-quality images by integrating semantic image restoration techniques.
Why It Matters
As low-quality images are prevalent in real-world applications, improving semantic segmentation in such conditions is crucial. This research addresses the limitations of traditional restoration methods and segmentation models, offering a solution that combines both for better performance in practical scenarios.
Key Takeaways
- RASS integrates semantic image restoration into the segmentation process.
- The Semantic-Constrained Restoration model enhances image reconstruction by aligning restoration and segmentation.
- RASS improves robustness to low-quality images through task-specific fine-tuning.
- Extensive experiments validate the effectiveness of RASS against state-of-the-art methods.
- The authors provide a new dataset for low-quality image segmentation with high-quality annotations.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.14042 (cs) [Submitted on 15 Feb 2026] Title:Restoration Adaptation for Semantic Segmentation on Low Quality Images Authors:Kai Guan, Rongyuan Wu, Shuai Li, Wentao Zhu, Wenjun Zeng, Lei Zhang View a PDF of the paper titled Restoration Adaptation for Semantic Segmentation on Low Quality Images, by Kai Guan and 5 other authors View PDF HTML (experimental) Abstract:In real-world scenarios, the performance of semantic segmentation often deteriorates when processing low-quality (LQ) images, which may lack clear semantic structures and high-frequency details. Although image restoration techniques offer a promising direction for enhancing degraded visual content, conventional real-world image restoration (Real-IR) models primarily focus on pixel-level fidelity and often fail to recover task-relevant semantic cues, limiting their effectiveness when directly applied to downstream vision tasks. Conversely, existing segmentation models trained on high-quality data lack robustness under real-world degradations. In this paper, we propose Restoration Adaptation for Semantic Segmentation (RASS), which effectively integrates semantic image restoration into the segmentation process, enabling high-quality semantic segmentation on the LQ images directly. Specifically, we first propose a Semantic-Constrained Restoration (SCR) model, which injects segmentation priors into the restoration model by aligning its cross-attention ...
