Dr. Jinzheng Lu Profile

Dr. Jinzheng Lu

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Proceedings Article | 2 December 2011 Paper

Image super-resolution enhancement based on online learning and blind sparse decomposition

Jinzheng Lu, Qiheng Zhang, Zhiyong Xu, Zhenming Peng

Proceedings Volume 8004, 80040B (2011) https://doi.org/10.1117/12.901540

KEYWORDS: Associative arrays, Image enhancement, Lawrencium, Image processing, Super resolution, Chemical species, Image resolution, Resolution enhancement technologies, Reconstruction algorithms, Databases

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This paper presents a different learning-based image super-resolution enhancement method based on blind sparse decomposition, in order to improve its resolution of a degraded one. Firstly, sparse decomposition based image super-resolution enhancement model is put forward according to the geometrical invariability of local image structures under different conditions of resolution. Secondly, for reducing the complexity of dictionary learning and enhancing adaptive representation ability of dictionary atoms, the over-complete dictionary is constructed using online learning fashion of the given low resolution image. Thirdly, since the fixed sparsity of the conventional matching pursuit algorithms for sparse decomposition can not fit all types of patches, the approach to sparse decomposition with blind sparsity can achieve relatively higher accurate sparse representation of an image patch. Lastly, atoms of high resolution dictionary and coefficients of representation of the given low-resolution image are synthesized to the desired SR image. Experimental results of the synthetic and real data demonstrate that the suggested framework can eliminate blurring degradation and annoying edge artifacts in the resulting images. The proposed method can be effectively applied to resolution enhancement of the single-frame low-resolution image.

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