KEYWORDS: Reconstruction algorithms, Associative arrays, Chemical species, Optimization (mathematics), Image restoration, Signal to noise ratio, Image quality, Magnetic resonance imaging, Magnetism, Detection and tracking algorithms
In order to improve the dictionary sparsity of image compression perception, enhance the noise suppression ability of the dictionary and improve the quality of image reconstruction, a k-singular value decomposition dictionary learning algorithm based on decision-making gray wolf optimization is proposed. In this method, the decision-making grey wolf optimization algorithm is introduced into the atom update stage of k-singular value decomposition dictionary learning algorithm to further optimize the atoms, so as to effectively improve the sparse representation performance of the dictionary. At the same time, a priori experience is introduced into the grey wolf optimization model to guide the optimization direction-making of the wolf pack, and the motion dimension of the wolf pack is reasonably limited. The experimental results show that in the two image data sets, the dictionary trained by the dictionary learning algorithm optimized by the decision-making wolf has stronger sparse representation ability of the image, better image reconstruction effect, and has certain significance to suppress the noise of the image itself.
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