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Image super-resolution via adaptive sparse representation and self-learning

Image super-resolution via adaptive sparse representation and self-learning

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This study proposes a novel super-resolution regularisation model based on adaptive sparse representation and self-learning frameworks. The fidelity term in the model ensures that the reconstructed image is consistent with the observation image. The adaptive sparsity regularisation term constrains the reconstructed image with an adaptive sparse representation, which successfully harmonises the sparse representation and the collaborative representation adaptively via producing suitable coefficients. To construct a more effective dictionary, the high-frequency features from the underlying image patches are extracted, and the dictionary learning and sparse representation are integrated. To this end, the alternating minimisation algorithm is used to divide this model into three subproblems, and the alternating direction method of multipliers and iterative back-projection method are used to solve the subproblems. To illustrate the effectiveness of the proposed method, additional experiments are conducted on some generic images. Compared with some state-of-the-art algorithms, the experimental results demonstrate that the proposed method achieves better results in terms of both visual quality and noise immunity.

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