摘要
为了解决图像超分辨率重建过程中出现的问题,结合图像的稀疏表示,增加控制邻近块兼容性的约束,建立具有邻近块兼容性约束的L1/2稀疏正则化模型.采用加权L2范式代替Lp(0<p<1)范式,对迭代加权最小二乘法进行转化,提出一种自适应正则化参数选取的算法.通过拼接字典的方法,训练出重要的特征并优化了重建图像的质量.实验结果表明,该重建方法在去噪和保留边缘信息方面具有较好的效果,重建的高分辨率图像在视觉上具有清晰锐利的特点,而且在峰值信噪比和结构相似度两项指标上都优于传统的重建方法.
In order to solve the ill-posing problem and poor effect of fixed regularization parameter in superresolution image reconstruction,an adaptive regularization combining the study of sparse representation is proposed.By additional restrictions for compatibility of adjacent patches,a new L1/2non-convex optimization model is built.Reweighted L2Norm rather than Lp(0<p<1)Norm is applied into the adaptive algorithm for adjustment of regularization parameter.With the help of joint dictionary training method,some important features for improving the quality of reconstructed image are obtained.Experimental results show that the method has significant advantages in denoising and preserving edge details.It is showed that the proposed method not only makes the desired high-resolution images visually clearer,but it also outperforms some traditional methods in both the value of peak signal to noise ratio and structural similarity.
作者
叶向荣
刘怡俊
陈云华
熊炯涛
Ye Xiang-rong;Liu Yi-jun;Chen Yun-hua;Xiong Jiong-tao(School of Computer Science, Guangdong University of Technology;School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China)
出处
《广东工业大学学报》
CAS
2017年第6期43-48,共6页
Journal of Guangdong University of Technology
基金
广东省自然科学基金资助项目(2014A030310169
2016A030313713)
广东省科技计划项目(2016B090918126
2016B090904001
2014B090901061
2015B090901060
2015B090908001
2015B090903080)
广州市科技计划项目(2014Y2-00211)
关键词
L1/2非凸优化
稀疏表示
自适应正则化
超分辨率重建
邻近块兼容性
拼接字典
L1/2 non-convex optimization
sparse representation
adaptive regularization
super-resolution reconstruction
compatibility of adjacent patches
jointing dictionary
