Interactive Feature Embedding for Infrared and Visible Image Fusion

• URL: http://arxiv.org/abs/2211.04877v1
• Date: Wed, 9 Nov 2022 13:34:42 GMT
• Title: Interactive Feature Embedding for Infrared and Visible Image Fusion
• Authors: Fan Zhao and Wenda Zhao and Huchuan Lu
• Abstract summary: General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention. We propose a novel interactive feature embedding in self-supervised learning framework for infrared and visible image fusion.
• Score: 94.77188069479155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention by utilizing elaborately designed loss functions. However, the unsupervised mechanism depends on a well designed loss function, which cannot guarantee that all vital information of source images is sufficiently extracted. In this work, we propose a novel interactive feature embedding in self-supervised learning framework for infrared and visible image fusion, attempting to overcome the issue of vital information degradation. With the help of self-supervised learning framework, hierarchical representations of source images can be efficiently extracted. In particular, interactive feature embedding models are tactfully designed to build a bridge between the self-supervised learning and infrared and visible image fusion learning, achieving vital information retention. Qualitative and quantitative evaluations exhibit that the proposed method performs favorably against state-of-the-art methods.

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