摘要
变分正则化方法基本思想是分析和把握图像的先验知识,将图像的正则化复原问题转变成极小化能量泛函问题。根据图像的有界变差和稀疏性先验知识,提出一个基于全变差（TV）正则化和稀疏性约束的耦合图像复原模型。模型通过全变差图像模型、图像Curvelet变换下稀疏性和数据保真模型的联合优化,达到图像边缘结构和纹理特征的保持。并且给出基于算子分裂法的求解算法,实验证明该算法复原图像的视觉质量优于快速TV复原算法（FTVdG）的复原结果。
The basic idea of the variation regularization method is to analyze and grasp the priori knowledge of the image. Then the regularization restoration of the image is turned into the problem of minimizing the energy functional. According to the bounded variation and sparisty constraints of the image , total variation （TV） regularization image restoration couple model based on sparsity constraint is proposed. This model is composed of TV regularization restoration model, curvelet transform sparsity of the image and data fidelity model. It is good at restoring the edge and texture of the image. Combined with operator splitting method, there is a given numerical algorithm. Experiments show that the algorithm is superior to the fast TV （FTVdG） algorithm in visual quality of the restored image.
作者
邵俊
肖亮
SHAO Jun, XIAO Liang （1. Lu＇an Central Branch of the People＇s Bank of China, Lu＇an 237006, China; 2.School of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, China）
出处
《电脑知识与技术》
2011年第8期5415-5417,5429,共4页
Computer Knowledge and Technology
基金
高等学校博士学科点专项科研基金资助课题（20070288050）
