DenseFuse is a new approach for infrared and visible image fusion. Considering the single encoding strategy of DenseFuse, we propose a dual-encoder DenseNet (DEDNet), which develops a heterogenous image dual-encoder and a channel picking/Gaussian filtering based fusion strategy. The proposed method includes encoding layers, fusion strategy and decoder, in which encoding layers consist of dual encoders. Since infrared and visible images have different imaging mechanisms, the dual encoders can extract the features of infrared and visible images more effectively and improve the quality of fused images. The fusion strategy based on l1-norm's channel selection and Gaussian filtering improves the structural integrity and spatial correlation of the fused features. In the DEDNet, the infrared image is input to the infrared encoder to get the infrared features while the visible image is input to the visible encoder to get the visible features. Then, the fusion strategy fuses the infrared and visible features structurally and spatially. Finally, the decoder reconstructs the fused features to obtain the fused image. Experiments show that the DEDNet achieves competitive results in both subjective and objective evaluation metrics compared with other fusion approaches.
Salient object detection(SOD) is particularly important especially for applications like autonomous driving which requires real-time inference speed and high performance. Most of the previous works however focus on global object accuracy but not on the connection of local objects. In this paper, we first process the cityscapes dataset into a saliency detection dataset, which focuses on distinguishing between moving objects on the road and moving objects on the sidewalk. In order to enable the saliency detection network to learn the connection between the target categories, we propose a gated convolution(GCov), which can control the input of the feature layer. For the evaluation of SOD, we combine a variety of loss functions to form a mixed loss. Equipped with the GCov and mixed loss, the proposed architecture is able to effectively distinguish the difference in the semantics of the location for the targets of the same category. Experimental results on the dataset show that our method has competitive results compared with other saliency detection networks.
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