Single-image super-resolution reconstruction based on global non-zero gradient penalty and non-local Laplacian sparse coding
Autor: | Weihong Li, Feiyu Pan, Jinming Li, Weiguo Gong |
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Rok vydání: | 2014 |
Předmět: |
business.industry
Texture (cosmology) Applied Mathematics ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Zero (complex analysis) Pattern recognition Sparse approximation Image (mathematics) Computational Theory and Mathematics Artificial Intelligence Component (UML) Signal Processing Computer Vision and Pattern Recognition Artificial intelligence Enhanced Data Rates for GSM Evolution Electrical and Electronic Engineering Statistics Probability and Uncertainty business Neural coding Laplace operator ComputingMethodologies_COMPUTERGRAPHICS Mathematics |
Zdroj: | Digital Signal Processing. 26:101-112 |
ISSN: | 1051-2004 |
Popis: | Methods based on sparse coding have been successfully used in single-image super-resolution reconstruction. However, they tend to reconstruct incorrectly the edge structure and lose the difference among the image patches to be reconstructed. To overcome these problems, we propose a new approach based on global non-zero gradient penalty and non-local Laplacian sparse coding. Firstly, we assume that the high resolution image consists of two components: the edge component and the texture component. Secondly, we develop the global non-zero gradient penalty to reconstruct correctly the edge component and the non-local Laplacian sparse coding to preserve the difference among texture component patches to be reconstructed respectively. Finally, we develop a global and local optimization on the initial image, which is composed of the reconstructed edge component and texture component, to remove possible artifacts. Experimental results demonstrate that the proposed approach can achieve more competitive single-image super-resolution quality compared with other state-of-the-art methods. |
Databáze: | OpenAIRE |
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