Acta Optica Sinica, Volume. 40, Issue 16, 1610001(2020)
Fusion of Low-Illuminance Visible and Near-Infrared Images Based on Convolutional Neural Networks
Herein, for low-illumination application scenes, an end-to-end convolutional neural network (CNN) is proposed for the fusion of near-infrared (NIR) and visible images. The fused image can combine the signal-to-noise ratio of an NIR image and the color of visible images. To verify the capability of CNN for practical fusion tasks, a real dataset with accurate registration was collected. Moreover, the training set was preprocessed via information fusion, thereby enabling the network to extract additional information from NIR images. Experimental results reveal that the proposed method is superior to existing fusion methods in terms of visual quality and quantitative measurements.
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Chaoying Tang, Shiliang Pu, Pengzhao Ye, Fei Xiao, Huajun Feng. Fusion of Low-Illuminance Visible and Near-Infrared Images Based on Convolutional Neural Networks[J]. Acta Optica Sinica, 2020, 40(16): 1610001
Category: Image Processing
Received: Apr. 14, 2020
Accepted: May. 11, 2020
Published Online: Aug. 7, 2020
The Author Email: Pu Shiliang (pushiliang.hri@hikvision.com)