A Task-guided, Implicitly-searched and Meta-initialized Deep Model for
  Image Fusion
Risheng Liu Zhu Liu Jinyuan Liu Xin Fan Zhongxuan Luo
Image fusion plays a key role in a variety of multi-sensor-based vision
systems, especially for enhancing visual quality and/or extracting aggregated
features for perception. However, most existing methods just consider image
fusion as an individual task, thus ignoring its underlying relationship with
these downstream vision problems. Furthermore, designing proper fusion
architectures often requires huge engineering labor. It also lacks mechanisms
to improve the flexibility and generalization ability of current fusion
approaches. To mitigate these issues, we establish a Task-guided,
Implicit-searched and Meta-initialized (TIM) deep model to address the image
fusion problem in a challenging real-world scenario. Specifically, we first
propose a constrained strategy to incorporate information from downstream tasks
to guide the unsupervised learning process of image fusion. Within this
framework, we then design an implicit search scheme to automatically discover
compact architectures for our fusion model with high efficiency. In addition, a
pretext meta initialization technique is introduced to leverage divergence
fusion data to support fast adaptation for different kinds of image fusion
tasks. Qualitative and quantitative experimental results on different
categories of image fusion problems and related downstream tasks (e.g., visual
enhancement and semantic understanding) substantiate the flexibility and
effectiveness of our TIM. The source code will be available at
https://github.com/LiuZhu-CV/TIMFusion.