[1]葛广重,杨敏.基于稀疏表示的单幅图像超分辨率重建[J].计算机技术与发展,2013,(09):113-116.
　GE Guang-zhong,YANG Min.Single Image Super-resolution Reconstruction Based on Sparse Representation[J].,2013,(09):113-116.
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基于稀疏表示的单幅图像超分辨率重建()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年09期
页码:
113-116
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Single Image Super-resolution Reconstruction Based on Sparse Representation
文章编号:
1673-629X（2013）09-0113-04
作者:
葛广重; 杨敏
南京邮电大学 自动化学院
Author(s):
GE Guang-zhong; YANG Min
关键词:
稀疏表示; 图像超分辨率; 学习字典; 稀疏编码
Keywords:
sparse representation; image super-resolution; learned dictionary; sparse coding
文献标志码:
A
摘要:
针对单幅低分辨率灰度图像，提出一种基于稀疏表示和字典学习的超分辨率重建算法，通过选择合适的过完备字典，图像块可表示为字典元素的稀疏线性组合。对于输入的低分辨率图像，寻求每一图像块的稀疏表示，利用此表示系数产生高分辨率图像输出。为消除Elad方法重建图像中产生的黑色边缘并提高重建图像的质量，文中在稀疏表示方法的基础上利用反向投影法对其进行改进。仿真实验结果表明，改进算法不仅实现了上述目的，而且在图像信噪比和算法运行效率上都有所提高，从而达到了算法改进的目的
Abstract:
Present a super-resolution reconstruction approach to single gray image based on sparse representation and dictionary learning. Image patches can be well-represented as a sparse linear combination of dictionary's elements from an appropriately chosen over-com-plete dictionary. For each patch of the low-resolution input image,seek a sparse representation and then use the coefficients of this repre-sentation to generate the high-resolution output image. In this paper,in order to eliminate the black edge and improve the image's quali-ty,introduce the back-projection into the Elad's super-resolution reconstruction. The results of simulation experiment show the method not only achieves the above purpose,but also lead to a marked improvement both in PSNR ( Peak Signal to Noise Ratio) and operating efficiency

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更新日期/Last Update: 1900-01-01