THE INFRARED AND VISIBLE IMAGE FUSION ALGORITHM BASED ON RESNET50 AND CONVOLUTION SPARSE REPRESENTATION
This paper proposes an infrared and visible image fusion algorithm based on ResNet50 neural network and convolution sparse representation.The infrared and visible images were decomposed into the base layers and the detail layers through low-pass filtering.The convolution sparse representation was utilized to process the base layer to obtain the fused base layer,and the ResNet50 neural network was applied to extract the features of the detail layer.For the feature map,we performed L1-regularization and choose-max strategy to obtain the maximum weight layer.And the fused detail layer could be generated through weight distribution.We generated the new base layer and detail layer to obtain the fused image.We proposed new fusion strategies for the base layer and the detail layer,and the fused image retained more detail information and structural information.The experimental results show that our method is superior to the comparison algorithm in subjective and objective metrics.
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