基于结构自相似性和形变块特征的单幅图像超分辨率算法

doi:10.11772/j.issn.1001-9081.2018061230

计算机应用 ›› 2019, Vol. 39 ›› Issue (1): 275-280.DOI: 10.11772/j.issn.1001-9081.2018061230

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于结构自相似性和形变块特征的单幅图像超分辨率算法

向文, 张灵, 陈云华, 姬秋敏

广东工业大学计算机学院, 广州 511400

收稿日期:2018-06-13 修回日期:2018-08-24 出版日期:2019-01-10 发布日期:2019-01-21
通讯作者: 向文
作者简介:向文(1993-),男,湖南怀化人,硕士研究生,CCF会员,主要研究方向:图像处理、模式识别;张灵(1968-),女,广西南宁人,教授,博士,主要研究方向:数据挖掘、计算机视觉、无线传感网;陈云华(1977-),女,湖北仙桃人,讲师,博士,主要研究方向:计算机视觉、模式识别、深度学习;姬秋敏(1992-),女,山东济宁人,硕士研究生,主要研究方向:图像处理、模式识别。
基金资助:
广东省自然科学基金面上项目（2016A030313703）；广东省自然科学基金资助项目（2016A030313713）；广东省科技计划项目（2016B030305002）；广东省交通运输厅科技项目（科技-2016-02-030）。

Single image super resolution algorithm based on structural self-similarity and deformation block feature

XIANG Wen, ZHANG Ling, CHEN Yunhua, JI Qiumin

College of Computer Science, Guangdong University of Technology, Guangzhou Guangdong 511400, China

Received:2018-06-13 Revised:2018-08-24 Online:2019-01-10 Published:2019-01-21
Supported by:
This work is partially supported by the Surface Program of National Natural Science Foundation of Guangdong Province (2016A030313703), the Natural Science Foundation of Guangdong Province (2016A030313713), the Science and Technology Program of Guangdong Province (2016B030305002), the Science and Technology Project of Guangdong Provincial Transportation Department (Science and Technology-2016-02-030).

摘要/Abstract

摘要： 针对单幅图像超分辨率（SR）复原样本资源不足和抗噪性差的问题，提出一种基于结构自相似和形变块特征的单幅图像超分辨率算法。首先，该方法通过构建尺度模型，尽可能地扩展搜索空间，克服单幅图像超分辨率训练样本不足的缺陷；接着，通过样例块的几何形变提升了局限性的内部字典大小；最后，为了提升重建图片的抗噪性，利用组稀疏学习字典来重建图像。实验结果表明：与Bicubic、稀疏字典学习（ScSR）算法和基于卷积神经网络的超分辨率（SRCNN）等优秀字典学习算法相比，所提算法可以得到主观视觉效果更为清晰和客观评价更高的超分辨率图像，峰值信噪比（PSNR）平均约提升了0.35 dB。另外所提算法通过几何形变的方式扩展了字典规模和搜索的准确性，在算法时间消耗上平均约减少了80 s。

关键词: 形变块, 块匹配, 字典学习, 自相似性, 组稀疏

Abstract: To solve the problem of insufficient sample resources and poor noise immunity for single image Super Resolution (SR) restoration, a single image super-resolution algorithm based on structural self-similarity and deformation block feature was proposed. Firstly, a scale model was constructed to expand search space as much as possible and overcome the shortcomings of lack of a single image super-resolution training sample. Secondly, the limited internal dictionary size was increased by geometric deformation of sample block. Finally, in order to improve anti-noise performance of reconstructed picture, the group sparse learning dictionary was used to reconstruct image. The experimental results show that compared with the excellent algorithms such as Bicubic, Sparse coding Super Resolution (ScSR) algorithm and Super-Resolution Convolutional Neural Network (SRCNN) algorithm, the super-resolution images with more subjective visual effects and higher objective evaluation can be obtained, the Peak Signal-To-Noise Ratio (PSNR) of the proposed algorithm is increased by about 0.35 dB on average. In addition, the scale of dictionary is expanded and the accuracy of search is increased by means of geometric deformation, and the time consumption of algorithm is averagely reduced by about 80 s.

Key words: deformation block, block matching, dictionary learning, self-similarity, group sparseness

中图分类号:

TP391.413

向文, 张灵, 陈云华, 姬秋敏. 基于结构自相似性和形变块特征的单幅图像超分辨率算法[J]. 计算机应用, 2019, 39(1): 275-280.

XIANG Wen, ZHANG Ling, CHEN Yunhua, JI Qiumin. Single image super resolution algorithm based on structural self-similarity and deformation block feature[J]. Journal of Computer Applications, 2019, 39(1): 275-280.

参考文献

[1] FREEMAN W T, JONES T R, PASZTOR E C. Example based super resolution[J]. IEEE Computer Graphics and Applications, 2002, 22(2):56-65.
[2] CHANG H, YEUNG D Y, XIONG Y. Super-resolution through neighbor embedding[C]//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2004:I-275-I-282.
[3] YANG J, WRIGHT J, HUANG T, et al. Image super-resolution via spare representation[J]. IEEE Transactions on Image Processing, 2010, 19(11):2861-2873.
[4] YANG C Y, YANG M H. Fast direct super-resolution by simple functions[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2013:561-568.
[5] TIMOFTE R, SMET V, GOOL L. Anchored neighborhood regression for fast example-based super-resolution[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2013:1920-1927.
[6] TIMOFTE R, SMET V, GOOL L. A+:adjusted anchored neighborhood regression for fast super resolution[C]//Proceedings of the 2014 Asian Conference on Computer Vision. Berlin:Spring, 2014:111-126.
[7] DONG C, LOY C C, HE K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(2):295-307.
[8] MEHRAN E, VRSCAY E R. Solving the inverse problem of image zooming using "Self-Examples"[M]//Image Analysis and Recognition. Berlin:Springer, 2007:117-130.
[9] GKASNER D, BAGON S, IRANI M. Super-resolution from a single image[C]//Proceedings of the 2009 International Conference on Computer Vision. Piscataway, NJ:IEEE, 2009:349-356.
[10] FREEDMAN G, FATTAL R. Image and video upscaling from local self-examples[J]. ACM Transactions on Graphics, 2011,30(2):1-11.
[11] SINGH A, AHUJA N. Super-resolution using sub-band self-similarity[C]//ACCV 2014:Proceedings of the 2014 Asian Conference on Computer Vision. Berlin:Springer, 2014:552-568.
[12] ZONTAK M, IRANI M. Internal statistics of a single natural image[C]//CVPR '11:Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2011:977-984.
[13] 向文,张灵,陈云华,等.结合结构自相似性和卷积网络的单幅图像超分辨率[J].计算机应用,2018,38(3):854-858.(XIANG W, ZHANG L, CHEN Y H, et al. Combined with structural self-similarity and convolution of a single image super resolution[J]. Journal of Computer Applications, 2018, 38(3):854-858.)
[14] 肖进胜,刘恩雨,朱力,等.改进的基于卷积神经网络的图像超分辨率算法[J].光学学报,2017,37(3):103-111.(XIAO J S, LIU E Y, ZHU L, et al. Improved image super-resolution algorithm based on convolutional neural network[J]. Acta Optica Sinica,2017,37(3):103-111.)
[15] HUANG J B, KANG S B, AHUJA N, et al. Image completion using planar structure guidance[J]. ACM Transactions on Graphics, 2014, 33(4):1-10.
[16] FREEMAN W T, JONES T R, PASZTOR E C. Example-based super-resolution[J]. IEEE Computer Graphics & Applications, 2002, 22(2):56-65
[17] YANG J, WRIGHT J, HUANG T, et al. Image super-resolution as sparse representation of raw image patches[C]//CVPR 2008:Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2008:1-8.
[18] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322.
[19] DARABI S, SHECHTMAN E, BARNES C, et al. Image melding:combining inconsistent images using patch-based synthesis[J]. ACM Transactions on Graphics, 2012, 31(4):1-10.
[20] HACOHEN Y, FATTAL R, LISCHINSKI D. Image upsampling via texture hallucination[C]//Proceedings of the 2010 IEEE International Conference on Computational Photography. Piscataway, NJ:IEEE, 2010:1-8.
[21] BARNES C, SHECHTMAN E, FINKELSTEIN A, et al. PatchMatch:a randomized correspondence algorithm for structural image editing[J]. ACM Transactions on Graphics, 2009, 28(3):1-11.
[22] YANG J, LIN Z, COHEN S. Fast image super-resolution based on in-place example regression[C]//Proceedings of the 2013 Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2013:1059-1066.
[23] KIM K I, KWON Y. Single-image super-resolution using sparse regression and natural image prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010,32(6):1127-1133.

基于结构自相似性和形变块特征的单幅图像超分辨率算法

Single image super resolution algorithm based on structural self-similarity and deformation block feature

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	葛晨宇, 董良, 许伊昆, 常毅, 张宏鸣. 基于总变分低秩组稀疏的全球雷达数据修复算法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3353-3361.
[2]	林腾涛, 查思明, 陈蕾, 龙显忠. 图趋势过滤诱导的噪声容错多标记学习模型[J]. 计算机应用, 2021, 41(1): 8-14.
[3]	张丽, 孔旭, 孙忠贵. 基于非局部自相似性和低秩矩阵逼近的补全算法[J]. 计算机应用, 2020, 40(11): 3327-3331.
[4]	李南云, 王旭光, 吴华强, 何青林. 基于灰度塔评分的匹配模型构建在无人机网络视频拼接中的应用[J]. 计算机应用, 2019, 39(5): 1480-1484.
[5]	徐涛, 王晓明. 泛化误差界指导的鉴别字典学习[J]. 计算机应用, 2019, 39(4): 940-948.
[6]	陶永鹏, 景雨, 顼聪. 基于分组字典与变分模型的图像去噪算法[J]. 计算机应用, 2019, 39(2): 551-555.
[7]	王丽芳, 王雁丽, 蔺素珍, 秦品乐, 高媛. 基于改进的Zernike矩的局部描述符与图割离散优化的非刚性多模态脑部图像配准[J]. 计算机应用, 2019, 39(2): 582-588.
[8]	张雯雯, 韩裕生. 基于非局部自相似性的低秩稀疏图像去噪[J]. 计算机应用, 2018, 38(9): 2696-2700.
[9]	杨晓玲, 李志清, 刘雨桐. 基于多标签判别字典学习的图像自动标注[J]. 计算机应用, 2018, 38(5): 1294-1298.
[10]	向文, 张灵, 陈云华, 姬秋敏. 结合结构自相似性和卷积网络的单幅图像超分辨率[J]. 计算机应用, 2018, 38(3): 854-858.
[11]	邹佳彬, 孙伟. 基于提升静态小波变换与联合结构组稀疏表示的多聚焦图像融合[J]. 计算机应用, 2018, 38(3): 859-865.
[12]	冯辉, 荆晓远, 朱小柯. 基于多视图特征投影与合成解析字典学习的图像分类[J]. 计算机应用, 2017, 37(7): 1960-1966.
[13]	李争名, 杨南粤, 岑健. 基于原子Fisher判别准则约束字典学习算法[J]. 计算机应用, 2017, 37(6): 1716-1721.
[14]	杨学峰, 程耀瑜, 王高. 基于小波域压缩感知的遥感图像超分辨算法[J]. 计算机应用, 2017, 37(5): 1430-1433.
[15]	孙少超. 基于带权核范数最小化和混合高斯模型的去噪模型[J]. 计算机应用, 2017, 37(5): 1471-1474.