摘要
TV-Wavelet-L1(TVWL1)模型因包含全变分(Total-variation,TV)和小波正则化约束,具有较强的图像重建能力。而传统求解TVWL1模型的算法往往忽略了综合/分析稀疏表示方法的方式。本文提出了一个新的求解TVWL1模型的图像重建算法,该算法把图像重建问题分解为几个子问题并交替求解,利用分析稀疏表示特性构建子问题的求解算法。实验结果表明,与已有算法相比,本文提出的算法可以提高重建图像主客观质量。
TV-Wavelet-LI（TVWL1） model which consists of total-variation （TV） and wavelet regularization has great capability in image reconstruction. However, traditional algorithms solving the TVWL1 model for image reconstruction ignore the way of synthesis/analysis sparse representation. A new image reconstruction algorithm is thus proposed to solve TVWL1, where the original signal reconstruction problem is decomposed into multiple much simpler sub-problems which can be solved alternately. In addition, the analysis sparse representation is considered in a sub-problem. Experimental results demonstrate that the proposed algorithm can obviously improve both objective and subjective qualities of reconstruction images com- pared with the existing algorithms.
出处
《数据采集与处理》
CSCD
北大核心
2014年第1期30-35,共6页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(61390512
61033004
61170103
61370118)资助项目
