Interactive Feature Embedding for Infrared and Visible Image Fusion

Article; Early Access

关键词: MULTI-FOCUS;    SPARSE REPRESENTATION;    SHEARLET TRANSFORM;    DECOMPOSITION;    ENHANCEMENT;    INFORMATION;    FRAMEWORK;   

DOI  :  10.1109/TNNLS.2023.3264911

来源: SCIE

【 摘 要 】

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 a self-supervised learning framework for infrared and visible image fusion, attempting to overcome the issue of vital information degradation. With the help of a 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 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.

【 授权许可】

Free