【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。更多还原

【Abstract】 TV-Wavelet-L1(TVWL1)model which consists of total-variation(TV)and wavelet regularization has great capability in image reconstruction.However,traditional algorithms solving the TVWL1model 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 subproblems 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 compared with the existing algorithms.更多还原