Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive ***,in low-light scenarios,the illumination degradation o...
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Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive ***,in low-light scenarios,the illumination degradation of visible light images makes it difficult for existing fusion methods to extract texture detail information from the *** this time,relying solely on the target saliency information provided by infrared images is far from *** address this challenge,this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement,named *** method is based on the improvement of the MobileOne Block,using the Edge-MobileOne Block embedded with the Sobel operator to perform feature extraction and downsampling on the source *** intermediate features at different scales obtained are then fused by a cross-modal attention fusion *** addition,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is used for image enhancement of both infrared and visible light images,guiding the network model to learn low-light enhancement capabilities through enhancement *** completion of network training,the Edge-MobileOne Block is optimized into a direct connection structure similar to MobileNetV1 through structural reparameterization,effectively reducing computationalresource ***,after extensive experimental comparisons,our method achieved improvements of 4.6%,40.5%,156.9%,9.2%,and 98.6%in the evaluation metrics Standard Deviation(SD),Visual Information Fidelity(VIF),Entropy(EN),and Spatial Frequency(SF),respectively,compared to the best results of the compared algorithms,while only being 1.5 ms/it slower in computation speed than the fastest method.
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