HP-UniIF: Hierarchical Prompt Learning for Unified Image Fusion

2026-08-22

AI Summary

本文提出HP-UniIF框架，通过层次化提示学习方法解决图像融合、退化恢复和任务导向感知问题，实现多目标解耦与性能提升。

Abstract

General image fusion seeks to integrate complementary information from multiple source images, yet real-world applications often require a single system to support heterogeneous fusion, degradation restoration, and task-oriented perception simultaneously. Existing unified frameworks struggle with these orthogonal objectives, resulting in entangled representations and degraded performance across subtasks. We propose HP-UniIF, a unified vision framework that leverages diffusion priors to bridge heterogeneous fusion, visual restoration, and downstream perception. To address the limited adaptability of diffusion models to domain-, degradation-, and task-level objectives within one pipeline, HP-UniIF introduces a depth-wise hierarchical conditional modulation strategy that decouples these objectives across network stages. Task prompt modulation at bottleneck layers adapts the backbone to different fusion paradigms, the degradation prompt router at shallow layers injects degradation-aware constraints for local restoration, and the application prompt bank at decoding stages aligns generation with downstream tasks. This hierarchical design enables HP-UniIF to produce visually faithful results while preserving task-relevant semantics. Extensive experiments across multiple fusion tasks, diverse degradations, and various downstream applications demonstrate the superior performance of HP-UniIF.

Problem

• image fusion
• heterogeneous fusion
• degradation restoration
• task-oriented perception

Innovation

• Hierarchical Prompt Learning
• Diffusion Priors
• Conditional Modulation
• Degradation Restoration
• Unified Image Fusion