PGE-SAM: Prompt-Guided Feature Enhancement for Interactive Segmentation under Degradation
Segment Anything Model (SAM) has revolutionized promptable image segmentation with strong zero-shot generalization. However, its performance degrades substantially under real-world imaging artifacts such as noise, blur, and compression. Existing methods restore features globally without focusing on segmentation-relevant regions and neglect SAM's iterative refinement mechanism, leading to suboptimal performance in interactive settings. We propose Prompt-Guided Feature Enhancement SAM (PGE-SAM), a framework that explicitly leverages user prompts and prior mask predictions to spatially guide the feature restoration process toward regions of interest through a Prompt Guidance Generator. To re...
| Authors | Tuan-Duc Nguyen, Anh-Tuan Mai, Duc-Trong Le |
|---|---|
| Primary category | cs.CV |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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