L_1-L_2正则化的图像复原交替优化算法  

L_1-L_2regularization based alternating optimal algorithm for image restoration

作　　者：肖宿[1] 洪留荣[1] 沈克[1] 郑颖[1] 

机构地区：[1]淮北师范大学计算机科学与技术学院,安徽淮北235000

出　　处：《计算机工程与应用》2014年第3期125-128,154,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61070090);安徽省高等学校省级自然科学研究项目(No.KJ2013B237)

摘　　要：为快速且准确地重建原始图像,提出一种新的图像复原算法。在稀疏表示的框架下,建立图像复原问题的约束优化模型,同步估计原始图像及其稀疏表示。复原模型的目标函数包含L1-L2双正则项,为此采用交替优化将模型分解为若干子问题,交替迭代求解这些子问题。其中不可微分的子问题,由迭代重加权方法进行处理。实验结果表明,仅需较少次迭代该算法即可获得原始图像及其稀疏表示的最优估计。与某些优秀的同类算法相比,该算法的速度更快,复原图像的质量更高。For rapidly and accurately restoring images, a novel image restoration algorithm is presented. In the framework of sparse representation, a new constrained optimization model is created, which enables the estimations of the original image and its sparse representation. The objective function of the model has L1-L2 regularized terms, thus the alternating optimization method is introduced to decompose the model into equivalent sub-problems. The non-differentiable one among sub-problems is handled by iterative reweighted method. The experimental results demonstrate that with only a few itera-tions, the presented algorithm can achieve optimal estimations of the original image and its sparse representation. Com-pared with some state-of-the-art algorithms, the presented algorithm shows to be faster, and obtains better results.

关 键 词：图像复原 交替优化 稀疏表示 正则化 迭代重加权方法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]