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

Abstract

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.