摘要
为获得较高质量的恢复图像,采用一种改进的KSVD(MKSVD)算法对低分辨率(LR)图像进行消噪处理,进一步对消噪结果采用一种改进的POCS(MPOCS)算法进行恢复,得到边缘特征更清晰的恢复图像。MKSVD是一种典型的稀疏字典学习方法,具有自适应消噪特性,可有效减少特征系数的训练时间;MPOCS采用双边滤波器实现POCS算法中点扩散函数(PSF)的估计,较好保持了图像的边缘细节。采用模拟和真实的LR图像进行测试,仿真结果表明了该图像恢复方法的有效性。
To obtain high quality restored images,low resolution images were denoised using a modified K-mean singular decomposition(MKSVD)method,denoised images were restored using a modified POCS method to obtain restored images with clearer edge features.MKSVD is a typical sparse dictionary learning method and has better self-adaptive denoising property.This algorithm can efficiently reduce the training time of feature coefficients.In MPOCS algorithm,the bilateral filter was used to estimate the point spread function(PSF)of POCS,the image edge details were retained well.In test,the simulated and real low resolution images were used,and compared with restored results obtained using KSVD and POCS,experimental results verify that the method proposed is indeed effective.
作者
苏品刚
孙站里
尚丽
SU Pin-gang;SUN Zhan-li;SHANG Li(College of Electronic Information Engineering,Suzhou Vocational University,Suzhou 215104,China;College of Electrical Engineering and Automation,Anhui University,Hefei 230039,China)
出处
《计算机工程与设计》
北大核心
2018年第9期2800-2804,2821,共6页
Computer Engineering and Design
基金
国家自然科学基金项目(61373098
61370109)
关键词
凸集投影(POCS)方法
双边滤波
稀疏字典
改进KSVD算法
快速稀疏编码(FSC)
图像恢复
projection onto convex sets(POCS)method
bilateral filter
sparse dictionary
modified K-mean singular decomposition(MKSVD)algorithm
fast sparse coding(FSC)
image restoration
