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基于离线双字典学习算法的图像超分辨率重建研究

Image Super Resolution Reconstruction Based on Offline Double Dictionary Learning Algorithm

周琳   杨娜  
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摘要

为了提高图像超分辨率重建的质量,采用离线双字典学习算法.首先图像块建立字典稀疏模型,确定字典中原子数量;然后使用基于离线字典学习对图像稀疏编码,同时把稀疏编码统一到一个框架中进行优化编码;接着对字典进行分解多个子字典,将图像块中像素点的列向量在子字典展开;最后双字典与超分辨率重构中不同分辨率的异构数据进行同构化,确定控制残差条件,给出了算法实现过程.实验仿真显示本文算法重建效果清楚,峰值信噪比最大,BIQI 最小.

Abstract

In order to improve the quality of image super-resolution reconstruction,offline double dictionary learning algorithm is established.First,dictionary sparse representation model was built by image block,and the number of atoms in dictionary is determined.Second,sparse code of image is used based on offline dictionary learning and put into frame to optimize code.Third,double dictionary is decomposed into some sub dictionaries,and column vector of pixel of image block is spread in sub dictionary.Last,heterogeneous data with different resolution in the final dictionary learning and super resolution reconstruction is getting isomorphic,control residual is determined,and process is given.Simulation shows that ODDL algorithm reconstruction result is clear,PSNR is better,and BIQI is lower.

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补充资料

中图分类号:TP393

所属栏目:图像处理与仿真

基金项目:河南省教育厅“十五”教育科学规划课题,编号:2005-JKGHAZ-086;河南省社科联课题,编号:SKL-2011-1927.

收稿日期:2014-12-09

修改稿日期:2015-02-04

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周琳:河南牧业经济学院, 河南 郑州 450045
杨娜:河南牧业经济学院, 河南 郑州 450045

备注:周琳(1977-),女,河南西华人,硕士,实验师,主要研究方向:计算机教育技术研究.

【1】Qiegen Liu,Shanshan Wang,Jianhua Luo.A novel predual dictionary learning algorithm[J].Journal of Visual Communication and Image Representation,2012,23(1):182-193.

【2】Q.Barthélemy,A.Larue,J.I.Mars.Decomposition and dictionary learning for 3D trajectories[J].Signal Processing,2014,98(5):423-437.

【3】江静,张雪松.图像超分辨率重建算法综述[J].红外技术,2012,34(1):24-30.
Jiang Jing,Zhang Xue-song.A Review of Super-resolution Reconstruction Algorithms[J].Infrared Technology,2012,34(1):24-30.

【4】Jianchao Yang,Zhaowen Wang,Zhe Lin,et al.Coupled dictionary training for image super-resolution[J].IEEE Transactions onImage Processing,2012,21(8):3467-3478.

【5】Mingli Song,Chun Chen,Jiajun Bu,et al.Image-based facial sketch-to-photo synthesis via online coupled dictionary learning[J].Information Sciences,2012,193(15):233-246.

【6】Roman Zeyde,Michael Elad,Matan Protter.On Single Image Scale-Up Using Sparse-Representations[C]//7th International Conferences on Curves and Surfaces,Avignon,France,2010:711-730.

【7】S.F.Cotter,K.Kreutz-Delgado,B.D.Rao.Backward sequential elimination for sparse vector subset selection[J].Signal Processing,2001,81(9):1849-1864.

【8】Qidan Zhu,Lei Sun,Chengtao Cai.Non-local neighbor embedding for image super-resolution through FoE features[J].Neurocomputing,2014,141(10):211-222.

【9】余雷,满家巨,刘利刚.基于联合字典学习的图像去噪[J].湖南师范大学自然科学学报,2013,36(6):11-16.
Yu Lei,Man Jiaju,Liu Ligang.Image Denoising via Joint-Dictionary Learning[J].Journal of Natural Science of Hunan Normal University,2013,36(6):11-16.

【10】Liu Liu,Zhen weiShi.Airplane detection based on rotation invariant and sparse coding in remote sensing images[J].Optik-International Journal for Light and Electron Optics,2014,125(9):5327-5333.

【11】梁锐华,成礼智.基于小波域字典学习方法的图像双重稀疏表示[J].国防科技大学学报,2012,34(4):126-131.
Liang Ruihua,Cheng Lizhi.Double sparse image representation via learning dictionaries in wavelet domain[J].Journal of National University of Defense Technology,2012,34(4):126-131.

【12】李民,程建,汤万琼.基于学习字典的图像类推方法[J].计算机应用研究,2011,28(8):3171-3173.
Li Min,Cheng Jian,Tang Wanqiong.Image analogies method based on learned dictionary[J].Application Research of Computers,2011,28(8):3171-3173.

【13】段菲,章毓晋.一种面向稀疏表示的最大间隔字典学习算法[J].清华大学学报:自然科学版,2012,52(4):566-570.
Duan Fei,Zhang Yujin.Max-margin learning algorithm for sparse representation[J].Journal of Tsinghua University (Natural Science Edition),2012,52(4):566-570.

【14】罗燕龙.基于局部稀疏表示模型的在线字典学习跟踪算法研究[D].福建:厦门大学,2014:45-60.
Luo YanLong.Representation of learning tracking model based on local sparse Online Dictionary[D].FuJian:Xiamen University,2014:45-60.

【15】马路,邓承志,汪胜前,等.特征保留的稀疏表示图像去噪[J].计算机应用,2013,33(5):1416-1419.
Ma Lu,Deng Chengzhi,Wang Shengqia,et al.Feature-retained image de-noising via sparse representation[J].Journal of Computer Applications,2013,33(5):1416-1419.

【16】Ender M.Eksioglu.Online dictionary learning algorithm with periodic updates and its application to image denoising[J].Expert Systems with Applications,2014,41(8):3682-3690.

【17】彭真明,景亮,何艳敏,等.基于多尺度稀疏字典的多聚焦图像超分辨融合[J].光学精密工程,2014,22(1):169-175.
Peng Zhenming,Jing Liang,He Yan min,et al.Superresolution fusion of multi-focus image based on multiscale sparse dictionary[J].Editorial Office of Optics and Precision Engineeri,2014,22(1):169-176.

引用该论文

ZHOU Lin,YANG Na. Image Super Resolution Reconstruction Based on Offline Double Dictionary Learning Algorithm[J]. Infrared Technology, 2015, 37(4): 277-282

周琳,杨娜. 基于离线双字典学习算法的图像超分辨率重建研究[J]. 红外技术, 2015, 37(4): 277-282

被引情况

【1】姚敏,周勤. 一种低匹配误差敏感度的红外图像超分辨率算法. 红外技术, 2016, 38(10): 864-869

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