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自适应半耦合稀疏字典学习算法
Adaptive semi-coupled sparse dictionary learning algorithm
投稿时间:2016-10-23  修订日期:2018-01-09
中文关键词:自适应聚类  稀疏表示  超分辨力  半耦合字典学习  图像处理
英文关键词:adaptive clustering  sparse representation  super resolution  semi-coupled sparse dictionary learning  image processing
基金项目:国家自然科学基金资助项目(61271330;61411140248;61473198);四川省科技支撑计划资助项目(2014GZ0005)
作者单位
沈志伟 College of Electronic InformationSichuan UniversityChengdu Sichuan 610065China 
杨晓敏 College of Electronic InformationSichuan UniversityChengdu Sichuan 610065China 
吴 炜 College of Electronic InformationSichuan UniversityChengdu Sichuan 610065China 
胡明明 College of Electronic InformationSichuan UniversityChengdu Sichuan 610065China 
摘要点击次数: 122
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中文摘要:
      为实现图像超分辨力重建,提出了一个自适应半耦合稀疏字典学习算法。由于耦合字典学习算法中存在稀疏编码约束条件太过严苛的缺点,本文采用半耦合的字典学习算法。根据在半耦合的字典学习算法中全局字典表达的局限性,分析和采用了多字典训练算法及相应的重建方法。提出了基于自适应图像块聚类算法的半耦合稀疏字典学习算法。仿真实验结果显示,新算法重建得到的Butterfly,Cameraman,Foreman,Plants,Hat和Lena等图像的峰值信噪比(PSNR)分别比用基于K-means聚类算法的半耦合稀疏字典学习算法得到的重建图像高出0.18 dB,0.16 dB,0.52 dB,0.21 dB,0.23 dB和0.14 dB。该算法可以得到更好的图像重建效果。
英文摘要:
      In order to achieve higher resolution images, a semi-coupled sparse dictionary learning algorithm by using adaptive image blocks clustering algorithm is proposed. The theoretical basis of semi-coupled sparse dictionary learning algorithm and adaptive image blocks clustering algorithm are studied in this paper. Firstly,according to the application of sparse representation theory in image super resolution algorithm,the coupled and semi-coupled sparse dictionary learning algorithm are introduced. The coupled dictionary learning algorithm assumes that sparse codings of corresponding high and low resolution image blocks are equal, but this assumption is too strict. The semi-coupled dictionary learning algorithm relaxes this assumption,assuming that sparse codings of corresponding high and low resolution image blocks are not equal,but satisfying a linear mapping. Secondly,because the semi-coupled sparse dictionary learning algorithm is more reasonable than the coupled sparse dictionary learning algorithm, in this paper,the semi-coupled sparse dictionary learning algorithm is adopted. Then, according to the limitations of expression of the global dictionary, the multi-dictionary learning algorithm is analyzed. Finally,through the analysis of the traditional image blocks clustering algorithm, a semi-coupled sparse dictionary learning algorithm by using adaptive image blocks clustering algorithm is proposed. The experimental results show that the Peak Signal to Noise Ratios(PSNR) of Butterfly, Cameraman, Foreman, Plants, Hat and Lena images obtained by the proposed algorithm are higher than that of the semi-coupled sparse dictionary learning algorithm based on K-means clustering algorithm by 0.18 dB,0.16 dB,0.52 dB,0.21 dB,0.23 dB and 0.14 dB respectively. According to the results,a conclusion is drawn that better reconstruction images can be obtained by the proposed method.
引用本文:沈志伟,杨晓敏,吴 炜,胡明明.自适应半耦合稀疏字典学习算法[J].太赫兹科学与电子信息学报,2018,16(3):529~534
DOI:10.11805/TKYDA201803.0529
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