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The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution (SR) and the special case of face hallucination. The M17-S was a commercial bullpup rifle sold by the Bushmaster Firearms company of Windham, Maine, from 1994 to 2005. Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). signed onto Int. In contrast to existing methods that involve the bicubic interpolation for pre-processing (which results in large feature maps), the proposed method directly extracts features from the low-resolution input space and thereby entails low computational loads. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. This paper deals with the single image scale-up problem using sparse-representation modeling. Building Consultants & Contractors, Building fabrication contractors, New Model Home Builders, Residential Contractors. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. The Book Contains The Elaborated And Clearly Defined Parametric And Non-Parametric Designs. This Is The Unique Compilation Of The Concepts, Processes, Practices And Applications Of The Total Quality Management. However, most models attempt to improve reconstruction accuracy while increasing the requirement of number of model parameters. This paper presents a new approach to single-image superresolution, based upon sparse signal representation. To this end, we introduce a novel network architecture for SISR, which strikes a good trade-off between reconstruction quality and low computational complexity. 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With very deep networks, however, convergence speed becomes a ones. Each LR feature vector in the input image is expressed as the weighted combination of its K nearest neighbors in the dictio-nary; the corresponding HR feature vector is reconstructed under the assumption that the local LR embedding is preserved. train extremely deep rectified models directly from scratch and to investigate Furthermore , our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction , thereby facilitates resource-aware applications. The DenseNet obtains significant improvements over the state-of-the-art on all five of them (e.g., yielding 3.74% test error on CIFAR-10, 19.25% on CIFAR-100 and 1.59% on SVHN). When asked if the drones were authorized to drop bombs on teachers who exhibit inadequacy, Chester E. Finn, Jr., president of the Thomas B. Fordham Foundation, replied, "Don't be ridiculous. A must read article about the standardized test industry.

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