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Single-image super-resolution (SR) imaging is a fundamental problem in image processing; it is important in entertainment, video surveillance, remote sensing, medicine, and other fields. Gaussian process regression (GPR) is a kernel method whereby nonlinear mapping relationships in data can be learned. However, the traditional Gaussian kernel function used in GPR is isotropic and fails to capture complex image structures. Accordingly, the structure information of image patches, termed steering kernel coefficients (SKCs), is extracted by a steering kernel function. After patches with similar structure are clustered according to their SKCs, an anisotropic automatic-relevance-determination (ARD) kernel function is used to learn the model for each cluster. Aiming at learning a structure-sensitive GPR model, we integrate the SKCs and ARD to achieve improved performance for GPR-based SR. Experiments demonstrate that the proposed method can effectively capture the structural relevance of image patches and yield promising results. [ABSTRACT FROM AUTHOR]

Due to the existence of a large number of redundant data in the process of covering multiple targets, the effective coverage of monitored region decreases, causing the network to consume more energy. To solve this problem, this paper proposes a multitarget k-degree coverage preservation protocol. Firstly, the affiliation between the sensor nodes and target nodes is established in the network model; meanwhile the method used to calculate the coverage expectation value of the monitored region is put forward; secondly, in the aspect of the network energy conversion, use scheduling mechanisms on the sensor nodes to balance the network energy and achieve different network coverage quality with energy conversion between different nodes. Finally, simulation results show that NMCP can improve the network lifetime by effectively reducing the number of active nodes to meet certain coverage requirements. [ABSTRACT FROM AUTHOR]