SCN_Matlab
Matlab reimplementation of SCNSR
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Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration problems. For image super-resolution, several models based on deep neural networks have been recently proposed and attained superior performance that overshadows all previous handcrafted models. The question then arises whether large-capacity and data-driven models have become the dominant solution to the ill-posed super-resolution problem. In this paper, we argue that domain expertise represented by the conventional sparse coding model is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results. We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end. The interpretation of the network based on sparse coding leads to much more efficient and effective training, as well as a reduced model size. Our model is evaluated on a wide range of images, and shows clear advantage over existing state-of-the-art methods in terms of both restoration accuracy and human subjective quality.
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With the development of the super-resolution convolutional neural networ...
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State-of-the-art approaches toward image restoration can be classified i...
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The last decade has shown a tremendous success in solving various comput...
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Inspired by the robustness and efficiency of sparse representation in sp...
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Recently, deep models have been successfully applied in several applicat...
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Deep convolutional neural networks (DCNN) have been widely adopted for
r...
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Low level image restoration is an integral component of modern artificia...
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