One-stop Multi-modality Image Dehazing-Registration-Fusion Framework via Progressive Task Cooperation

摘要

Infrared and visible image fusion (IVF) encounters two challenges: 1) image unregistered, where parallax leads to blurred fusion results, and 2) adverse imaging conditions, which introduces outlier data (such as haze) that significantly degrade image registered and fusion performance. Existing IVF methods fail to account for the joint impact of unregistered and haze factor, often neglecting these challenges or addressing them in isolation. However, unregistered and haze are intrinsically coupled in fusion task. For example, haze can obscure structural details, leading to inaccurate registration and ultimately degrading fusion performance. To address the intrinsic coupling between unregistered and haze, we propose a progressive task-cooperative processing pipeline—dehazing, registration, and fusion—to achieve robust fusion of unregistered images under hazy conditions. To mitigate the challenges posed by architectural complexity and excessive parameter overhead in task progressive learning, we propose a lightweight model approximation paradigm through hierarchical knowledge distillation. The framework employs a stage-wise distillation optimization strategy that synergistically integrates: 1) primary task-specific distillation for modality-aware feature extraction, and 2) progressive task-cooperative distillation for fusion-oriented representation learning, which can improve fusion robustness for unregistered multi-modal inputs in hazy conditions. Extensive experiments demonstrate that our method achieves significantly superior performance compared to the State-Of-The-Art (SOTA) methods.