PGE-SAM: Prompt-Guided Feature Enhancement for Interactive Segmentation under Degradation

• URL: http://arxiv.org/abs/2606.30477v1
• Date: Mon, 29 Jun 2026 15:37:47 GMT
• Title: PGE-SAM: Prompt-Guided Feature Enhancement for Interactive Segmentation under Degradation
• Abstract summary: Segment Anything Model (SAM) has revolutionized promptable image segmentation with strong zero-shot generalization.<n>Existing methods restore features globally without focusing on segmentation-relevant regions.<n>We propose Prompt-Guided Feature Enhancement SAM (PGE-SAM) to spatially guide the feature restoration process toward regions of interest.
• Score: 3.769829978199937
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 recover fine-grained details lost under degradation, we introduce Multi-Scale Features Interaction to incorporate low-level encoder features, along with a Foreground Reconstruction Loss that restricts feature-level supervision to the segmentation target. Furthermore, we present DM-Seg, a benchmark for interactive segmentation on degraded medical images, spanning multiple imaging modalities with both general and modality-specific degradations at varying severity levels. Extensive experiments demonstrate that PGE-SAM achieves SOTA robustness on both medical and natural image domains across multiple degradation levels, while maintaining generalization to clean images and adding less than one-fifth of the parameters of prior methods.

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