Because of the poor lighting conditions at night time, visible images are often fused with corresponding infrared (IR) images for context enhancement of the scenes in night vision. In this paper, we present a novel night-vision context enhancement algorithm through IR and visible image fusion with the guided filter. First, to enhance the visibility of poorly illuminated details in the visible image before the fusion, an adaptive enhancement method is developed by incorporating the processes of dynamic range compression and contrast restoration based on the guided filter. Then, a hybrid multi-scale decomposition based on the guided filter is introduced to inject the IR image information into the visible image through a multi-scale fusion approach. Moreover, a perceptual-based regularization parameter selection method is used to determine the relative amount of the injected IR spectral features by comparing the perceptual saliency of the IR and visible image information. This fusion method can successfully transfer the important IR image information into the fused image, and simultaneously preserve the details and background scenery in the input visible image. Experimental results show that the proposed algorithm is able to achieve better context enhancement results in night vision.
Abstract. To control severe soil erosion on the Loess Plateau, China, a great number of soil conservation measures have been implemented since 1950s and subsequently, the "Grain for Green" project was implemented in 1999. The measures and the project resulted in a large scale land use/cover change (LUCC). Understanding the impacts of the measures and the project on streamflow, sediment load and their dynamic relation is essential because the three elements are closely related to the sustainable catchment management strategy on the Loess Plateau. The data for seven selected catchments in the middle reaches of the Yellow River were used and standardized with the precipitation and the controlling area for analysis. The nonparametric Mann-Kendall test and the Pettitt test were employed to detect trends and change points of the annual streamflow and annual sediment load. Simple linear regressions for the monthly streamflow and sediment load from May to October were made to express their relationship. Based on the change point identification and the time when the project began to be implemented on the Loess Plateau, the complete time for the data records was divided into three periods to compare the change degrees of streamflow, sediment load and their relation for the catchments.Results show that there are three types of responses in streamflow, sediment load, and their dynamic relations for the seven catchments. The effects of the LUCC on streamflow, sediment load, and their relationships are greatest in the three transition zone catchments followed by the two rocky mountain catchments. The effects are much weaker in the two loess hilly-gully catchments. In general, the change degrees for sediment load are much greater than those for streamflow, which results from the decreased streamflow and weakening trend of their dynamic relation period by period in catchments.
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