Text-IF: Leveraging Semantic Text Guidance for Degradation-Aware and
  Interactive Image Fusion
Xunpeng Yi Han Xu Hao Zhang Linfeng Tang Jiayi Ma
Image fusion aims to combine information from different source images to
create a comprehensively representative image. Existing fusion methods are
typically helpless in dealing with degradations in low-quality source images
and non-interactive to multiple subjective and objective needs. To solve them,
we introduce a novel approach that leverages semantic text guidance image
fusion model for degradation-aware and interactive image fusion task, termed as
Text-IF. It innovatively extends the classical image fusion to the text guided
image fusion along with the ability to harmoniously address the degradation and
interaction issues during fusion. Through the text semantic encoder and
semantic interaction fusion decoder, Text-IF is accessible to the all-in-one
infrared and visible image degradation-aware processing and the interactive
flexible fusion outcomes. In this way, Text-IF achieves not only multi-modal
image fusion, but also multi-modal information fusion. Extensive experiments
prove that our proposed text guided image fusion strategy has obvious
advantages over SOTA methods in the image fusion performance and degradation
treatment. The code is available at https://github.com/XunpengYi/Text-IF.