Chengzhi Deng
Nanchang Institute of Technology

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Image Super-Resolution Reconstruction Based On L1/2 Sparsity Chengzhi Deng; Juanjuan Liu; Wei Tian; Shengqian Wang; Huasheng Zhu; Shaoquan Zhang
Bulletin of Electrical Engineering and Informatics Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v3i3.284

Abstract

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.
Image Super-Resolution Reconstruction Based On L1/2 Sparsity Chengzhi Deng; Juanjuan Liu; Wei Tian; Shengqian Wang; Huasheng Zhu; Shaoquan Zhang
Bulletin of Electrical Engineering and Informatics Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (666.602 KB) | DOI: 10.11591/eei.v3i3.284

Abstract

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.
Image Super-Resolution Reconstruction Based On L1/2 Sparsity Chengzhi Deng; Juanjuan Liu; Wei Tian; Shengqian Wang; Huasheng Zhu; Shaoquan Zhang
Bulletin of Electrical Engineering and Informatics Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (666.602 KB) | DOI: 10.11591/eei.v3i3.284

Abstract

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.
Total Variation based Multivariate Shearlet Shrinkage for Image Reconstruction Chengzhi Deng; Saifeng Hu; Wei Tian; Min Hu; Yan Li; Shengqian Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: January 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Shearlet as a new multidirectional and multiscale transform is optimally efficient in representing images containing edges. In this paper, a total variation based multivariate shearlet adaptive shrinkage is proposed for discontinuity-preserving image denoising. The multivariate adaptive threshold is employed to reduce the noise. Projected total variation diffusion is used to suppress the pseudo-Gibbs and shearlet-like artifacts. Numerical experiments from piecewise-smooth to textured images demonstrate that the proposed method can effectively suppress noise and nonsmooth artifacts caused by shearlet transform. Furthermore, it outperforms several existing techniques in terms of structural similarity (SSIM) index, peak signal-to-noise ratio (PSNR) and visual quality. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.1868