A sparse dictionary model for image superresolution is presented, which unifies the feature patches of high-resolution (HR) and low-resolution images using sparse dictionary coding. This method builds a sparse association between middle-frequency and high-frequency image components and realizes simultaneously match searching and optimization methods. Comparison with sparse coding method shows sparse dictionary is more compact and effective. Sparse K-SVD algorithm is applied for optimization to speed up sparse coding. Some experiments with real images show that our method outperforms other learning-based super-resolution algorithms.
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