Learning a Deep Multi-Scale Feature Ensemble and an Edge-Attention Guidance for Image Fusion

计算机科学 人工智能 图像融合 特征学习 特征(语言学) 模式识别(心理学) 深度学习 集成学习 图像(数学) GSM演进的增强数据速率 融合 比例(比率) 计算机视觉 机器学习 哲学 物理 量子力学 语言学
作者
Jinyuan Liu,Xin Fan,Ji Jiang,Risheng Liu,Zhongxuan Luo
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology [Institute of Electrical and Electronics Engineers]
卷期号:32 (1): 105-119 被引量:237
标识
DOI:10.1109/tcsvt.2021.3056725
摘要

Image fusion integrates a series of images acquired from different sensors, e.g. , infrared and visible, outputting an image with richer information than either one. Traditional and recent deep-based methods have difficulties in preserving prominent structures and recovering vital textural details for practical applications. In this article, we propose a deep network for infrared and visible image fusion cascading a feature learning module with a fusion learning mechanism. Firstly, we apply a coarse-to-fine deep architecture to learn multi-scale features for multi-modal images, which enables discovering prominent common structures for later fusion operations. The proposed feature learning module requires no well-aligned image pairs for training. Compared with the existing learning-based methods, the proposed feature learning module can ensemble numerous examples from respective modals for training, increasing the ability of feature representation. Secondly, we design an edge-guided attention mechanism upon the multi-scale features to guide the fusion focusing on common structures, thus recovering details while attenuating noise. Moreover, we provide a new aligned infrared and visible image fusion dataset, RealStreet, collected in various practical scenarios for comprehensive evaluation. Extensive experiments on two benchmarks, TNO and RealStreet, demonstrate the superiority of the proposed method over the state-of-the-art in terms of both visual inspection and objective analysis on six evaluation metrics. We also conduct the experiments on the FLIR and NIR datasets, containing foggy weather and poor light conditions, to verify the generalization and robustness of the proposed method.
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