Restoration Adaptation for Semantic Segmentation on Low Quality Images

• URL: http://arxiv.org/abs/2602.14042v1
• Date: Sun, 15 Feb 2026 08:13:23 GMT
• Title: Restoration Adaptation for Semantic Segmentation on Low Quality Images
• Authors: Kai Guan, Rongyuan Wu, Shuai Li, Wentao Zhu, Wenjun Zeng, Lei Zhang,
• Abstract summary: In real-world scenarios, the performance of semantic segmentation often deteriorates when processing low-quality (LQ) images.<n>We propose a Semantic-trained Restoration (SCR) model, which injects segmentation priors into the restoration model.<n>Then, RASS transfers semantic restoration knowledge into segmentation through LoRA-based module merging and task-specific fine-tuning.
• Score: 29.60165376603045
• License: http://creativecommons.org/licenses/by/4.0/
• 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 maps with segmentation masks, encouraging semantically faithful image reconstruction. Then, RASS transfers semantic restoration knowledge into segmentation through LoRA-based module merging and task-specific fine-tuning, thereby enhancing the model's robustness to LQ images. To validate the effectiveness of our framework, we construct a real-world LQ image segmentation dataset with high-quality annotations, and conduct extensive experiments on both synthetic and real-world LQ benchmarks. The results show that SCR and RASS significantly outperform state-of-the-art methods in segmentation and restoration tasks. Code, models, and datasets will be available at https://github.com/Ka1Guan/RASS.git.

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