An Unsupervised Deep Neural Network for Image Fusion

doi:10.16182/j.issn1004731x.joss.20-1062

Abstract

Abstract:

Due to the low dynamic range of camera, can not be expressed in the different region of the high dynamic scene a single-exposure image. An unsupervised depth neural network is constructed to fuse the multi-exposure images into a high dynamic image. Based on the VGG-Net, encoding and decoding sub-networks are designed. Guided by the structural similarity of the images before and after fusion, a loss function suitable for image fusion is designed by introducing the weight factors based on the local image information, and the valid information of the different input images is given consideration. Compared with the other methods, the subjective visual experience and objective quantitative indicators of the fused images are improved significantly.

Key words: pattern recognition, high dynamic scene, image fusion, unsupervised deep network, loss function

CLC Number:

TP391.4

Peipei Zhou, Xinglin Hou. An Unsupervised Deep Neural Network for Image Fusion[J]. Journal of System Simulation, 2022, 34(6): 1267-1274.

Figures/Tables 5

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Table 1

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HDR场景	Shen11	Li13	Ma15	Hou16	DF19-add	DF19-L1	MEF-Net20	本文方法
Balloons	0.932 3	0.908 2	0.987 9	0.932 7	0.947 8	0.963 2	0.978 3	0.989 4
Belgium house	0.926 6	0.884 9	0.978 6	0.918 5	0.937 9	0.958 4	0.974 3	0.989 0
Chinese garden	0.922 9	0.917 9	0.993 5	0.923 5	0.941 3	0.985 3	0.987 1	0.993 3
House	0.925 2	0.830 6	0.964 2	0.916 5	0.901 3	0.765 7	0.935 0	0.966 5
Landscape	0.899 8	0.905 2	0.994 8	0.921 9	0.984 2	0.990 8	0.981 6	0.989 4
Lighthouse	0.912 8	0.914 9	0.984 3	0.933 4	0.952 5	0.953 3	0.969 2	0.986 3
Madison capitol	0.893 5	0.800 5	0.981 2	0.871 2	0.914 0	0.906 6	0.958 1	0.984 1
Office	0.904 4	0.725 6	0.983 9	0.893 8	0.948 6	0.912 3	0.955 5	0.983 9
Room	0.920 6	0.853 3	0.978 2	0.915 1	0.938 4	0.939 6	0.967 8	0.987 1
Tower	0.912 8	0.858 6	0.986 0	0.910 1	0.915 1	0.921 9	0.959 1	0.988 5
Venice	0.903 9	0.834 9	0.987 9	0.904 6	0.935 9	0.961 4	0.965 7	0.982 7
Average	0.914 1	0.857 7	0.983 7	0.912 8	0.937 9	0.932 6	0.966 5	0.985 5

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