Infrared and visible image fusion based on convolutional neural network model and saliency detection via hybrid l0-l1 layer decomposition

Dong Liu; Dongming Zhou; Rencan Nie; Ruichao Hou

doi:10.1117/1.JEI.27.6.063036

27 December 2018 Infrared and visible image fusion based on convolutional neural network model and saliency detection via hybrid l0-l1 layer decomposition

Dong Liu, Dongming Zhou, Rencan Nie, Ruichao Hou

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 6, 063036 (December 2018). https://doi.org/10.1117/1.JEI.27.6.063036

Abstract

In the past few years, a convolutional neural network (CNN) based deep learning model has been broadly applied in image processing and computer vision. And different from other multiscale decomposition methods in infrared and visible image fusion field, a hybrid l₀-l₁ layer decomposition model, which combines the superiority of l₀ sparsity term and l₁ sparsity term, is carried out to decompose the image into the base layer and the detail layer. Thus, a CNN model and visual saliency-based methods are utilized to fuse the detail layer and the base layer, respectively. Finally, the experiments show that this combination of CNN and saliency detection fusion rule has outperform some of the existing methods in infrared and visible image fusion both subjective and objective evaluations.

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Dong Liu, Dongming Zhou, Rencan Nie, and Ruichao Hou "Infrared and visible image fusion based on convolutional neural network model and saliency detection via hybrid l0-l1 layer decomposition," Journal of Electronic Imaging 27(6), 063036 (27 December 2018). https://doi.org/10.1117/1.JEI.27.6.063036

Received: 30 June 2018; Accepted: 5 December 2018; Published: 27 December 2018

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