Bulletin of Electrical Engineering and Informatics
Vol 3, No 3: September 2014

Image Super-Resolution Reconstruction Based On L1/2 Sparsity

Chengzhi Deng (Nanchang Institute of Technology)
Juanjuan Liu (Jiangxi Science & Technology Normal University)
Wei Tian (Nanchang Institute of Technology)
Shengqian Wang (Nanchang Institute of Technology)
Huasheng Zhu (Nanchang Institute of Technology)
Shaoquan Zhang (Nanchang Institute of Technology)

Article Info

Publish Date
06 Jun 2014


Based on image sparse representation in the shearlet domain, we proposed a L1/2 sparsity regularized unconvex variation model for image super-resolution. The L1/2 regularizer term constrains the underlying image to have a sparse representation in shearlet domain. The fidelity term restricts the consistency with the measured imaged in terms of the data degradation model. Then, the variable splitting algorithm is used to break down the model into a series of constrained optimization problems which can be solved by alternating direction method of multipliers. Experimental results demonstrate the effectiveness of the proposed method, both in its visual effects and in quantitative terms.

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