Interactive Feature Embedding for Infrared and Visible Image Fusion

TL;DR

This paper introduces a self-supervised learning framework with interactive feature embedding for infrared and visible image fusion, effectively preserving vital information better than existing unsupervised methods.

Contribution

It proposes a novel interactive feature embedding approach within a self-supervised framework to enhance information retention in image fusion.

Findings

Outperforms state-of-the-art methods in qualitative evaluations

Achieves superior quantitative fusion metrics

Effectively preserves vital source image information

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.