Retina images are mainly obtained by Spectral Domain-Optical Coherence Tomography (SD-OCT), however, most of the acquired volume data are low-resolution(LR) images with noise, making it hard to quantify diseased tissue based on low quality retinal images. In this paper, we propose a denoising Semi-Coupled Dictionary Learning(SCDL) model to reconstruct the noise image while guaranteeing certain noise robustness. First, we use non-local similarities of retina images to construct constraint term, which is added to the objective function of the proposed model. Then, in order to guarantee the fidelity of reconstructed image, the initialized interpolation section should be replaced by the corresponding LR image after SR reconstruction. However, the noise in LR image will affects the reconstructed image quality. So we perform bilateral filtering on the LR image before replacement. Last, two sets of experiments on retinal noise images validate that our proposed method outperforms other state-of-the-art methods.
Here we analyzed many kinds of Net primary productivity (NPP) calculating methods and chose the Monteith model
based on the Light Utility Efficiency(LUE) to estimate the NPP value of Heilongjiang province in 2003. In this model,
the NOAA/AVHRR data, climate data and eradiation data were used to calculate the NPP with the support of GIS
technology. Then the spatial change of NPP was analyzed and the spatial map of NPP was drawn. The result showed that
the mean value of NPP in the study area is 329.2gC / (m<sup>2</sup>·a). There is a consistent trend between the spatial distribution
of NPP and temperature and rainfall. The NPP values of southeastern and central area are higher than that of western
drought area and Daxingan Mountains because of their better humidity and heat. The NPP values of different vegetation
have significant differences. They decrease in the sequence of forest, shrub, cropland and grassland. The NPP values
have significantly positive correlation with mean annual temperature, rainfall and NDVI.