Infrared and visible image fusion based on an improved gradient attention mechanism and the cross neural network
Since the current infrared and visible image fusion methods based on deep learning are difficult to distinguish important information from irrelevant information,a new infrared and visible image fusion method based on the gradient attention mechanism and the detail preserving cross network(DPCN)is proposed.First,an improved gradient attention mechanism is introduced into the DPCN to guide the network to focus on the texture details of the visible image and the target information of the infrared image as much as possible.The DPCN is used to enhance the information interaction between the infrared image and the visible image.Then,a decoder based on the multi-scale detail preserving module is proposed to reconstruct the merged features.Finally,an adaptive loss function based on an auxiliary discriminator is designed.The experimental results show that the fusion image of the proposed method can retain clearer edge and target information,and is superior to the compared methods in both subjective and objective evaluations.
国家科技期刊平台
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