Medical image fusion techniques can fuse medical images from different morphologies to make the medical diagnosis more reliable and accurate, which play an increasingly important role in many clinical applications. To obtain a fused image with high visual quality and clear structure details, this paper proposes a convolutional neural network (CNN) based medical image fusion algorithm. The proposed algorithm uses the trained Siamese convolutional network to fuse the pixel activity information of source images to realize the generation of weight map. Meanwhile, a contrast pyramid is implemented to decompose the source image. According to different spatial frequency bands and a weighted fusion operator, source images are integrated. The results of comparative experiments show that the proposed fusion algorithm can effectively preserve the detailed structure information of source images and achieve good human visual effects.
As one type of object detection, small object detection has been widely used in daily-life-related applications with many real-time requirements, such as autopilot and navigation. Although deep-learning-based object detection methods have achieved great success in recent years, they are not effective in small object detection and most of them cannot achieve real-time processing. Therefore, this paper proposes a single-stage small object detection network (SODNet) that integrates the specialized feature extraction and information fusion techniques. An adaptively spatial parallel convolution module (ASPConv) is proposed to alleviate the lack of spatial information for target objects and adaptively obtain the corresponding spatial information through multi-scale receptive fields, thereby improving the feature extraction ability. Additionally, a split-fusion sub-module (SF) is proposed to effectively reduce the time complexity of ASPConv. A fast multi-scale fusion module (FMF) is proposed to alleviate the insufficient fusion of both semantic and spatial information. FMF uses two fast upsampling operators to first unify the resolution of the multi-scale feature maps extracted by the network and then fuse them, thereby effectively improving the small object detection ability. Comparative experimental results prove that the proposed method considerably improves the accuracy of small object detection on multiple benchmark datasets and achieves a high real-time performance.
While water–energy–food (WEF) nexus is a major livelihood sources for local community, its security issues grow continually and there is limited information on how nexus resource management is effective at delivering livelihoods and food security. These difficulties are related to the lack of local community knowledge of the use and exploitation of water, energy, and food resources; this limited awareness leads to trade‐offs, especially in local and marginalized areas. On the basis of data collected from a local community through a survey‐based approach, this study examines local community perception of nexus resources and their contribution to livelihoods. The results indicate that community perceptions of nexus resources can be understood through social, natural, economic, human, physical, and environmental livelihood indicators. According to our findings, the perception of nexus resources is based on the benefits of individual resources rather than their interlinkages. This could be the result of community perceptions of a particular nexus resource from three nexus sector, that is, food. Food is the center of nexus resources for the community in the study area. This indicates, that there is a missing link between cross‐sectorial resource utilization and management, and full‐scale adoption of the WEF nexus to enhance living conditions. Our findings suggest that there is a low understanding of WEF nexus resource use and management, and the livelihood benefit of individual nexus resources is the primary focus in the studied community. From these results, we recommend more action to be taken by the government and other stakeholders to improve the local community perception of nexus resources for their livelihoods.
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