基于多尺度非局部约束的单幅图像超分辨率算法

潘宗序; 禹晶; 肖创柏; 孙卫东

doi:10.3724/SP.J.1004.2014.02233

基于多尺度非局部约束的单幅图像超分辨率算法

doi: 10.3724/SP.J.1004.2014.02233

1.
清华大学电子工程系北京 100084;
2.
北京工业大学计算机学院北京 100124

基金项目:

国家自然科学基金 (61171117),国家科技支撑计划项目 (2012BAH31B01), 中国博士后科学基金(2013M540946), 北京市教育委员会科技计划重点项目 (KZ201310028035)资助

详细信息

作者简介:
禹晶清华大学电子工程系博士后.2011 年获得清华大学电子工程系博士学位. 主要研究方向为图像处理与模式识别. E-mail: yujing@tsinghua.edu.cn

计量
- 文章访问数: 1673
- HTML全文浏览量: 72
- PDF下载量: 982
- 被引次数: 0
出版历程
- 收稿日期: 2013-05-07
- 修回日期: 2014-05-27
- 刊出日期: 2014-10-20

Single-image Super-resolution Algorithm Based on Multi-scale Nonlocal Regularization

1.
Department of Electronic Engineering, Tsinghua University, Beijing 100084;
2.
College of Computer Science and Technology, Beijing University of Technology, Beijing 100124

Funds:

Supported by National Natural Science Foundation of China (61171117), National Science and Technology Pillar Program of China (2012BAH31B01), the Postdoctoral Science Foundation of China (2013M540946) and Key Project of the Science and Technology Development Program of Beijing Education Committee of China (KZ201310028035)

摘要

摘要: 多尺度结构自相似性是指图像中的大量物体具有相同尺度以及不同尺度相似结构的性质.本文提出了一种基于多尺度非局部约束的单幅图像超分辨率算法,结合多尺度非局部方法和多尺度字典学习方法将蕴含在图像多尺度自相似结构中的附加信息加入到重建图像中.多尺度非局部方法在图像金字塔的不同层中搜索相似图像块,并利用多尺度相似图像块间的关系建立非局部约束项,通过正则化约束获取多尺度自相似结构中的附加信息;多尺度字典学习方法将图像金字塔作为字典学习的样本,通过字典学习使样本中的多尺度相似图像块在字典下具有稀疏表示形式,从而获取多尺度自相似结构中的附加信息.实验表明, 与ScSR、SISR、NLIBP、CSSS、ASDSAR和mSSIM等算法相比,本文的算法取得了更好的超分辨率重建效果.
- 超分辨率 /
- 多尺度结构自相似性 /
- 稀疏表示 /
- 非局部方法
Abstract: Multi-scale structural self-similarity refers to that there are many similar structures in the same image, which are either in the same scale or across different scales. In this paper, a single-image super-resolution method based on multi-scale nonlocal regularization is proposed. In this method, the multi-scale nonlocal and the multi-scale dictionary learning methods are combined to add the extra information exploited from multi-scale similar structures into the reconstructed image. The multi-scale nonlocal method exploits extra information from multi-scale similar structures by searching for similar patches in the image pyramid and constructing the multi-scale nonlocal regularization according to the correspondence between multi-scale similar patches. The multi-scale dictionary learning method exploits extra information from multi-scale similar structures by using the image pyramid as training samples in dictionary learning, so that the patches in the pyramid have sparse representations over the learned dictionary. Experimental results demonstrate that the method achieves better image quality compared with ScSR, SISR, NLIBP, CSSS, ASDSAR and mSSIM methods.
- Super-resolution (SR) /
- multi-scale structural self-similarity /
- sparse representation /
- nonlocal method

HTML全文

参考文献(18)

[1]	Sun Yan-Yue, He Xiao-Hai, Song Hai-Ying, Chen Wei-Long. A block-matching image registration algorithm for video super-resolution reconstruction. Acta Automatica Sinica, 2011, 37(1): 37-43(孙琰玥, 何小海, 宋海英, 陈为龙. 一种用于视频超分辨率重建的块匹配图像配准方法. 自动化学报, 2011, 37(1): 37-43)
[2]	An Yao-Zu, Lu Yao, Zhao Hong. An adaptive-regularized image super-resolution. Acta Automatica Sinica, 2012, 38(4): 601-608(安耀祖, 陆耀, 赵红. 一种自适应正则化的图像超分辨率算法. 自动化学报, 2012, 38(4): 601-608)
[3]	[3] Sen P, Darabi S. Compressive image super-resolution. In: Proceedings of 43rd Asilomar Conference on Signals, Systems and Computers. Pacific Grove, USA: IEEE, 2009. 1235-1242
[4]	[4] Yang J C, Wright J, Huang T S, Ma Y. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873
[5]	[5] Protter M, Elad M, Takeda H, Milanfar P. Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Transactions on Image Processing, 2009, 18(1): 36-51
[6]	[6] Dong W S, Zhang L, Shi G M, Wu X L. Nonlocal back-projection for adaptive image enlargement. In: Proceedings of the 2009 IEEE International Conference on Image Processing. Cairo, Egypt: IEEE, 2009. 349-352
[7]	[7] Glasner D, Bagon S, Irani M. Super-resolution from a single image. In: Proceedings of the 12th International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009. 349-356
[8]	[8] Dong W S, Zhang L, Shi G M, Wu X L. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Transactions on Image Processing, 2011, 20(7): 1838-1857
[9]	[9] Pan Z X, Yu J, Huang H J, Hu S X, Zhang A W, Ma H B, Sun W D. Super-resolution based on compressive sensing and structural self-similarity for remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(9): 4864-4876
[10]	Pan Zong-Xu, Yu Jing, Hu Shao-Xing, Sun Wei-Dong. Single image super resolution based on multi-scale structural self-similarity. Acta Automatica Sinica, 2014, 40(4): 594-603(潘宗序, 禹晶, 胡少兴, 孙卫东. 基于多尺度结构自相似性的单幅图像超分辨率算法. 自动化学报, 2014, 40(4): 594-603)
[11]	Engan K, Aase S O, Husoy J H. Method of optimal directions for frame design. In: Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Phoenix, AZ, USA: IEEE, 1999. 2443-2446
[12]	Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322
[13]	Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 2006, 15(12): 3736-3745
[14]	Gribonval R, Nielsen M. Sparse representations in unions of bases. IEEE Transactions on Information Theory, 2003, 49(12): 3320-3325
[15]	Daubechies I, Defrise M, De Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics, 2004, 57(11): 1413-1457
[16]	Moorthy A K, Bovik A C. A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters, 2010, 17(5): 513-516
[17]	Marziliano P, Dufaux F, Winkler S, Ebrahimi T. A no-reference perceptual blur metric. In: Proceedings of the 2002 International Conference on Image Processing. Rochester, NY, USA: IEEE, 2002. III-57-III-60
[18]	Xu L, Jia J Y. Two-phase kernel estimation for robust motion deblurring. In: Proceedings of the 11th European Conference on Computer Vision. Heraklion, Crete, Greece: Springer, 2010. 157-170

施引文献

资源附件(0)

访问统计

计量

文章访问数: 1673
HTML全文浏览量: 72
PDF下载量: 982
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

基于多尺度非局部约束的单幅图像超分辨率算法

doi: 10.3724/SP.J.1004.2014.02233

作者简介:
禹晶清华大学电子工程系博士后.2011 年获得清华大学电子工程系博士学位. 主要研究方向为图像处理与模式识别. E-mail: yujing@tsinghua.edu.cn

计量

Single-image Super-resolution Algorithm Based on Multi-scale Nonlocal Regularization

计量

目录

留言板

基于多尺度非局部约束的单幅图像超分辨率算法

doi: 10.3724/SP.J.1004.2014.02233

作者简介: 禹晶 清华大学电子工程系博士后.2011 年获得清华大学电子工程系博士学位. 主要研究方向为图像处理与模式识别. E-mail: yujing@tsinghua.edu.cn

计量

出版历程

Single-image Super-resolution Algorithm Based on Multi-scale Nonlocal Regularization

计量

出版历程

目录

作者简介:
禹晶清华大学电子工程系博士后.2011 年获得清华大学电子工程系博士学位. 主要研究方向为图像处理与模式识别. E-mail: yujing@tsinghua.edu.cn