AI 中文总结
HP-UniIF是一种基于分层条件调制策略的统一视觉框架，利用扩散先验实现异构图像融合、视觉恢复与下游感知的协同，在多任务实验中表现出优越性能。
AI 中文摘要
通用图像融合旨在整合多源图像的互补信息，但实际应用常要求单一系统同时支持异构融合、退化恢复及任务导向感知。现有统一框架难以兼顾这些正交目标，导致表示纠缠、子任务性能下降。本文提出HP-UniIF，一种利用扩散先验桥接异构融合、视觉恢复与下游感知的统一视觉框架。为解决扩散模型在单流程内对域、退化、任务级目标适应性有限的问题，HP-UniIF引入深度分层条件调制策略，在网络各阶段解耦这些目标：瓶颈层的任务提示调制使骨干适配不同融合范式，浅层的退化提示路由注入局部恢复的退化感知约束，解码阶段的应用提示库使生成结果与下游任务对齐。该分层设计让HP-UniIF生成视觉保真结果的同时保留任务相关语义，在多个融合任务、多样退化及各类下游应用上的大量实验证明了其优越性能。
英文摘要
General image fusion aims to integrate complementary information from multiple source images, but existing methods often rely on task-specific models and struggle to maintain robust performance under diverse degradation conditions. In this paper, we propose UniDiffFusion, a unified diffusion framework for multi-task and degradation-robust image fusion. UniDiffFusion leverages the strong generative prior of a pretrained diffusion model to establish a shared fusion backbone across heterogeneous fusion tasks, while introducing task- and degradation-aware conditional adaptation to accommodate their distinct information-selection requirements. Specifically, we employ task prompt modulation to progressively adapt the shared diffusion representations to different fusion objectives, and develop a degradation prompt router to dynamically retrieve degradation-aware priors and restore corrupted source features before fusion. Furthermore, an application prompt bank is introduced to incorporate task-oriented semantic guidance for downstream applications, such as object detection and semantic segmentation, without altering the shared fusion and restoration pathways. The proposed framework is trained in a progressive manner to decouple fusion learning, degradation-aware restoration, and application-specific adaptation, thereby reducing interference among heterogeneous objectives. Extensive experiments on visible-infrared, multi-exposure, and multi-focus image fusion demonstrate that UniDiffFusion achieves superior fusion quality and robustness under both clean and degraded conditions. Moreover, UniDiffFusion consistently improves downstream detection and semantic segmentation performance, demonstrating its effectiveness as a unified diffusion framework for both perceptual fusion and task-oriented vision.
