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• W3138233798 startingPage "103707" @default.
• W3138233798 abstract "Extracting sufficient features with limited available information is essential for image fusion. However, most of the deep learning algorithms cannot fully extract the features. This paper proposes a novel infrared and visible images fusion method based on improved DenseNet, Max-Relevance and Min-Redundancy (mRMR) and zero-phase component analysis (ZCA). Firstly, the convolutional layer and Dense block are utilized to extract the dense features of the source images. Secondly, the mRMR operation is used to filter out the feature set that has the greatest correlation with the image category and the least redundancy between different features. Thirdly, the initial activity level maps are obtained by ZCA and l1-norm. Then, upsampling and soft-max are employed to create the weight maps. Finally, the fused image is obtained by weighted average fusion. Experimental results showed that the images fused with our method are visually natural. The proposed method showed a significant improvement on objective indicators than the current state-of-the-art." @default.
• W3138233798 created "2021-03-29" @default.
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• W3138233798 date "2021-06-01" @default.
• W3138233798 modified "2023-09-23" @default.
• W3138233798 title "An infrared and visible image fusion method based on improved DenseNet and mRMR-ZCA" @default.
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• W3138233798 doi "https://doi.org/10.1016/j.infrared.2021.103707" @default.
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