doi :10.3969/j.issn.1001-893x.2015.06.003引用格式:周芹,马志强单勇,等.自适应约束下的双边全变差正则化超分辨率重建[J].电讯技术,2015,55(6):599-604.[ZHOU Qin,
MA Zhiqiang,SHAN Yong,et al.Regularized Super-resolution Reconstruction Based on Bilateral Total Variation Model with Adaptive Con?straints[J].Telecommunication Engineering,2015,55(6):599-604.]
自适应约束下的双边全变差正则化超分辨率重建
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周 芹1,**,马志强1,单 勇1,党建国2
(1.空军工程大学信息与导航学院,西安710077;2.解放军94188部队,西安710077)摘 要:在经典的双边全变差(BTV )超分辨率重建中,加权系数和正则化参数的恒定性导致重建结果边缘保持能力受限三为此,提出了一种自适应约束的BTV 正则化先验模型三算法首先定义了图像的局部邻域残差均值以区分当前像素属于平坦区域还是边缘区域;然后针对加权系数的不变性导致边缘削弱的问题,利用边缘方向和垂直边缘方向扩散性的不同,设计自适应权重矩阵;最后根据代价函数的极值问题推导出迭代公式,从而进行图像的超分辨率重建,重建过程中采用自适应的方法确定正则化参数,以便求得代价函数的全局最优解,提高了算法的鲁棒性三实验结果表明:与双三次线性插值法和经典BTV 算法相比,该算法取得了更好的视觉效果和更高的峰值信噪比,更多地保留了图像的边缘细节信息三
关键词:图像超分辨率重建;正则化;双边全变差模型;自适应约束;边缘保持
中图分类号:TN911.73 文献标志码:A 文章编号:1001-893X (2015)06-0599-06
Regularized Super -resolution Reconstruction Based on Bilateral Total Variation Model with Adaptive Constraints
ZHOU Qin 1,MA Zhiqiang 1,SHAN Yong 1,DANG Jianguo 2(https://www.doczj.com/doc/5013230295.html,rmation and Navigation College,Air Force Engineering University,Xi′an 710077,China;2.Unit 94188of PLA,Xi′an 710077,China)Abstract :In the classical super-resolution reconstruction algorithm based on the Bilateral Total Variation (BTV)model,the ability of edge preserving is restricted due to the constancy of the weighted coefficient and the regularization parameter.To solve the problem,an adaptive regularization BTV model is proposed.Firstly,the algorithm defines the local neighborhood residual mean of the image to distinguish wheather the pixel belongs to flat area or edge area.Then,the adaptive weighting matrix is designed in case that the con?stant weighted coefficient leads to the weaken ability of edge preserving,which takes advantage of the dif?ferent diffusivity between the direction of the edge and the vertical of edge direction;Finally,iterative for?mula is deduced about the extremum problem of the cost function,so that the super-resolution reconstruc?tion can be achieved.In the process the adaptive regularization parameter is adopted in order to get the global optimal value of the cost function.The algorithm improves its robustness.The experimental results show that the proposed algorithm acquires the better visual effect and the higher peak signal-to-noise ratio (SNR),in addition,it retains the more image details information,compared with the super -resolution method based on classical BTV model and the bicubic method.Key words :image super-resolutionre reconstruction;regularization;bilateral total variation model;adaptive constraints;edge preserving
四995四第55卷第6期2015年6月电讯技术Telecommunication Engineering Vol.55,No.6June,2015***收稿日期:2015-01-12;修回日期:2015-04-30 Received date :2015-01-12;Revised date :2015-04-30
基金项目:陕西省自然科学基金资助项目(2013JM8025)Foundation Item :The Natural Science Fundation of Shaanxi Province (2013JM8025)通讯作者:lianyungang_zq@https://www.doczj.com/doc/5013230295.html, Corresponding author :lianyungang_zq@https://www.doczj.com/doc/5013230295.html,