@inproceedings{dea46e1dac6f41c7a0fd36e90cc8a302,
title = "Image reconstruction via L0 gradient and L1 wavelet coefficients minimization",
abstract = "In this paper, we address the problem of image reconstruction from highly undersampled Fourier measurements. In order to promote inherent sparsity in gradient and wavelet transform domain, we proposed a new reconstruction scheme via minimizing L0 norm of gradients and L1 norm of wavelet coefficients. L0 gradient minimization can control the number of non-zero gradients to enforce the sparsity in gradient, which results in edge preserving reconstruction. The reconstruction is casted into optimization framework and alternating direction method of multipliers (ADMM) algorithm is utilized to efficiently solve the proposed optimization problem. Experimental results demonstrate the superior performance of the proposed method in comparison with the L1 gradient reconstruction method.",
keywords = "ADMM, Image reconstruction, L norm, Sparsity",
author = "Zexian Wang and Huiqian Du and Yilin Liu and Wenbo Mei",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 ; Conference date: 14-10-2017 Through 16-10-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/CISP-BMEI.2017.8302024",
language = "English",
series = "Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
editor = "Qingli Li and Lipo Wang and Mei Zhou and Li Sun and Song Qiu and Hongying Liu",
booktitle = "Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017",
address = "United States",
}