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A New Deep Learning Based Multi-Spectral Image Fusion Method

Department of Electrical Engineering, Hanyang University, Ansan 15588, Korea
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Entropy 2019, 21(6), 570; https://doi.org/10.3390/e21060570
Received: 12 May 2019 / Revised: 2 June 2019 / Accepted: 3 June 2019 / Published: 5 June 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
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Abstract

In this paper, we present a new effective infrared (IR) and visible (VIS) image fusion method by using a deep neural network. In our method, a Siamese convolutional neural network (CNN) is applied to automatically generate a weight map which represents the saliency of each pixel for a pair of source images. A CNN plays a role in automatic encoding an image into a feature domain for classification. By applying the proposed method, the key problems in image fusion, which are the activity level measurement and fusion rule design, can be figured out in one shot. The fusion is carried out through the multi-scale image decomposition based on wavelet transform, and the reconstruction result is more perceptual to a human visual system. In addition, the visual qualitative effectiveness of the proposed fusion method is evaluated by comparing pedestrian detection results with other methods, by using the YOLOv3 object detector using a public benchmark dataset. The experimental results show that our proposed method showed competitive results in terms of both quantitative assessment and visual quality. View Full-Text
Keywords: image fusion; visible; infrared; convolutional neural network; Siamese network image fusion; visible; infrared; convolutional neural network; Siamese network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Piao, J.; Chen, Y.; Shin, H. A New Deep Learning Based Multi-Spectral Image Fusion Method. Entropy 2019, 21, 570.

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