Searching a Hierarchically Aggregated Fusion Architecture for Fast Multi-Modality Image Fusion

Multi-modality image fusion refers to generating a complementary image that integrates typical characteristics from source images. In recent years, we have witnessed the remarkable progress of deep learning models for multi-modality fusion. Existing CNN-based approaches strain every nerve to design various architectures for realizing these tasks in an end-to-end manner. However, these handcrafted designs are unable to cope with the high demanding fusion tasks, resulting in blurred targets and lost textural details. To alleviate these issues, in this paper, we propose a novel approach, aiming at searching effective architectures according to various modality principles and fusion mechanisms. Specifically, we construct a hierarchically aggregated fusion architecture to extract and refine fused features from feature-level and object-level fusion perspectives, which is responsible for obtaining complementary target/detail representations. Then by investigating diverse effective practices, we composite a more flexible fusion-specific search space. Motivated by the collaborative principle, we employ a new search strategy with different principled losses and hardware constraints for sufficient discovery of components. As a result, we can obtain a task-specific architecture with fast inference time. Extensive quantitative and qualitative results demonstrate the superiority and versatility of our method against state-of-the-art methods.