CtrlFuse: Mask-Prompt Guided Controllable Infrared and Visible Image Fusion

TL;DR

CtrlFuse introduces a controllable infrared and visible image fusion method guided by mask prompts, enabling dynamic, task-specific fusion with improved segmentation accuracy and fusion quality.

Contribution

It presents a novel framework that integrates mask-guided prompts for interactive, controllable image fusion, surpassing existing methods in adaptability and performance.

Findings

Achieves state-of-the-art controllability in image fusion.

Outperforms existing methods in segmentation accuracy.

Enhances fusion quality through task-specific semantic guidance.

Abstract

Infrared and visible image fusion generates all-weather perception-capable images by combining complementary modalities, enhancing environmental awareness for intelligent unmanned systems. Existing methods either focus on pixel-level fusion while overlooking downstream task adaptability or implicitly learn rigid semantics through cascaded detection/segmentation models, unable to interactively address diverse semantic target perception needs. We propose CtrlFuse, a controllable image fusion framework that enables interactive dynamic fusion guided by mask prompts. The model integrates a multi-modal feature extractor, a reference prompt encoder (RPE), and a prompt-semantic fusion module (PSFM). The RPE dynamically encodes task-specific semantic prompts by fine-tuning pre-trained segmentation models with input mask guidance, while the PSFM explicitly injects these semantics into fusion features. Through synergistic optimization of parallel segmentation and fusion branches, our method achieves mutual enhancement between task performance and fusion quality. Experiments demonstrate state-of-the-art results in both fusion controllability and segmentation accuracy, with the adapted task branch even outperforming the original segmentation model.