Abstract
The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResNet-152 is proposed. First, the source images are decomposed into the low-frequency part and the high-frequency part. The low-frequency part is processed by the average weighting strategy. Second, the multi-layer features are extracted from high-frequency part by using the ResNet-152 network. Regularization L1, convolution operation, bilinear interpolation upsampling and maximum selection strategy on the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high-frequency as new high-frequency. Finally, the fusion image is reconstructed by the low-frequency and the high-frequency. Experiments show that the proposed method can obtain more details from the image texture by retaining the significant features of the images. In addition, this method can effectively reduce artifacts and noise. The consistency in the objective evaluation and visual observation performs superior to the comparative algorithms.
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The code and the test vector map data associated with this paper can be found at https://github.com/diylife/imagefusion_deeplearning.git.
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Acknowledgements
This work is funded by the Natural Science Foundation Committee, China (No. 41761080, and No. 41930101) and Industrial Support and Guidance Project of Gansu Colleges and Universities, No. 2019C-04.
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LZ conceived, designed, and also wrote the manuscript; HL performed the experiments; RZ supervised the study; PD offered helpful suggestions and reviewed the manuscript. RZ and PD analyzed and evaluated the results.
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Zhang, L., Li, H., Zhu, R. et al. An infrared and visible image fusion algorithm based on ResNet-152. Multimed Tools Appl 81, 9277–9287 (2022). https://doi.org/10.1007/s11042-021-11549-w
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DOI: https://doi.org/10.1007/s11042-021-11549-w