-norm sparse term to respectively capture the deep prior of the clean HSI, the spatial-spectral local smooth prior of the clean HSI, and the sparse prior of noise. The proposed S2DIP jointly leverages the expressive power brought from the deep convolutional neural network without any training da...
A description of specific problems inherent to the use of IRCCD detector arrays in such a thermal imager will be given, as well as the choosen solutions. Amont others, the high-linearity scanning system and the compensation for Fixed Pattern Noise (FPN) will be described. 展开 关键词:...
This has led to a steady focus on the functional and structural characteristics of the frontoparietal network (FPN) of areas involved with higher cognition and spatial behavior in humans, including the question of whether intelligence is correlated with larger or smaller activity in...
MDPrePost-Net is an end-to-end fully convolutional network (FCN) that consists of two main sub-models. The first sub-model is a pre-post deep feature extractor used to extract the spatial–spectral–temporal relationship between the pre, post, and mangrove conditions after the hurricane from ...
Several attempts have been taken in using dif- ferent sets of variables, from climate records to management parameters [7–9], and from geographical to spatial coordinates [10,11]. Some authors have also proposed the use of the estimates for contemporary group itself or some a posteriori ...
Image texture, defined as a function of the spatial variation in pixel intensities, has been widely applied in the quality evaluation and safety inspection of food and agricultural products. Thus, image texture analyses based on first-order and second-order statistics were also extracted from ...
The final saliency map in spatial domain is computed by the formula S(x) = F−1[exp((R(f) + P(f))]2, (3) where F−1 refers to the inverse Fourier transform, and P(f) denotes the phase spectrum of the frequency f. Based on the saliency map, the Otsu threshold is used...
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