Affiliation:
1. School of Information and Communication Engineering, Hainan University, Haikou 570228, China
2. State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China
Abstract
The aim of infrared (IR) and visible image fusion is to generate a more informative image for human observation or some other computer vision tasks. The activity-level measurement and weight assignment are two key parts in image fusion. In this paper, we propose a novel IR and visible fusion method based on the principal component analysis network (PCANet) and an image pyramid. Firstly, we use the lightweight deep learning network, a PCANet, to obtain the activity-level measurement and weight assignment of IR and visible images. The activity-level measurement obtained by the PCANet has a stronger representation ability for focusing on IR target perception and visible detail description. Secondly, the weights and the source images are decomposed into multiple scales by the image pyramid, and the weighted-average fusion rule is applied at each scale. Finally, the fused image is obtained by reconstruction. The effectiveness of the proposed algorithm was verified by two datasets with more than eighty pairs of test images in total. Compared with nineteen representative methods, the experimental results demonstrate that the proposed method can achieve the state-of-the-art results in both visual quality and objective evaluation metrics.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hainan Province
Major Science and Technology Project of Haikou City
Open Project Program of Wuhan National Laboratory for Optoelectronics
National Key Technology Support Program
Major Science and Technology Project of Hainan Province
Scientific Research Staring Foundation of Hainan University
Subject
General Earth and Planetary Sciences
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