Computer Science ›› 2020, Vol. 47 ›› Issue (9): 135-141.doi: 10.11896/jsjkx.190600146

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Single Image Super-resolution Algorithm Using Residual Dictionary and Collaborative Representation

TIAN Xu1, CHANG Kan1,2,3, HUANG Sheng1, QIN Tuan-fa1,2,3   

  1. 1 School of Computer and Electronic Information,Guangxi University,Nanning 530004,China
    2 Guangxi Key Laboratory of Multimedia Communications and Network Technology,Guangxi University,Nanning 530004,China
    3 Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing,Guangxi University,Nanning 530004,China
  • Received:2019-06-26 Published:2020-09-10
  • About author:TIAN Xu,born in 1993,postgrduate.His main research interests includeima-ge super-resolution and image denoi-sing.
    CHANG Kan,born in 1983,Ph.D,associate professor,master’s supervisor,is a member of China Computer Federation.His main research interests include image and video processing and compressive sensing,and video coding,etc.
  • Supported by:
    The work was supported by the National Natural Science Foundation of China (61761005,61761007) and Natural Science Foundation of Guangxi Zhuang Autonomous Region (2016GXNSFAA380154).

Abstract: Usually,the traditional single image super resolution (SR) algorithms generate the high resolution (HR) images with insufficient high-frequency information and blurred edges.To improve the quality of the reconstructed HR images,this paper proposes a single image SR algorithm by using residual dictionary and collaborative representation(Residual Dictionary and Collaborative Representation,RDCR).In the training phase,firstly,based on the ideas of dictionary learning and collaborative representation,a main dictionary and the corresponding main projection matrices are learned.After that,the reconstructed image samples are utilized to train multiple layers of residual dictionaries and residual projection matrices.In the testing phase,high-frequency information is gradually refined by reconstructing the residual information layer by layer.Extensive experimental results show that,at a scale factor of 4,the average peak signal-to-noise ratio (PSNR) values obtained by the proposed method on Set5 and Set14 are 0.20dB and 0.18dB higher than the traditional method A+,respectively.And the running time of the proposed method is close to that of A+.

Key words: Super resolution, Dictionary learning, Collaborative representation, Sparse representation

CLC Number: 

  • TP751
[1] DONG W S,ZHANG L,SHI G M,et al.Image Deblurring and super resolution by adaptive sparse domain selection and adaptive regularization [J].IEEE Transactions on Image Processing,2011,20(7):1838-1857.
[2] DONG W S,ZHANG L,SHI G M,et al.Nonlocally centralized sparse representation for image restoration [J].IEEE Transactions on Image Processing,2013,22(4):1620-1630.
[3] REN C,HE X,PU Y,et al.Enhanced non-local total variation model and multi-directional feature prediction prior for single image super resolution [J].IEEE Transactions on Image Processing,2019,28(8):3778-3793.
[4] CHANG K,ZHANG X,DING P L K,et al.Data-adaptive low-rank modeling and external gradient prior for single image super-resolution [J].Signal Processing,2019,161:36-49.
[5] FREEMAN W T,JONES T R,PASZTOR E C.Example based super-resolution [J].IEEE Computer Graphics and Applications,2002,22(2):56-65.
[6] YANG J C,WRIGHT J,HUANG T,et al.Image super resolution via sparse representation [J].IEEE Transactions on Image Processing,2010,19(11):2861-2873.
[7] ZEYDE R,ELAD M,PROTTER M.On single image scale-upusing sparse-representations[C]//International Conference on Curves and Surfaces.2010:711-730.
[8] TIMOFTE R,SMET V D,GOOL L V.Anchored neighborhood regression for fast example based super resolution[C]//International Conference on Computer Vision(ICCV).IEEE,2013:241-246.
[9] TIMOFTE R,SMET V D,GOOL L V.A+:adjusted anchored neighborhood regression for fast super resolution[C]//Asian Conference on Computer Vision (ACCV).IEEE,2014:111-126.
[10] TIMOFTE R,RASMUS R,GOOL L V.Seven ways to improve example based single image super resolution[C]//Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2016:375-379.
[11] ZHANG J,ZHAO C,XIONG R,et al.Image Super-Resolution via Dual-Dictionary Learning and Sparse Representation [C]//IEEE International Symposium on Circuits and Systems (ISCAS).2012:1688-1691.
[12] PAN Z X,YU J,XIAO C B,et al.Single Image Super Resolution Based on Adaptive Multi-Dictionary Learning [J].Acta Electronica Sinica,2015,43(2):209-216.
[13] WANG R G,LIU L L,YANG J,et al.Image super-resolution based on clustering and collaborative representation [J].Opto-Electronic Engineering,2018,45(4):9-18.
[14] QIU K,YI B S,XIANG M,et al.Collaborative Sparse Dictionary Learning for Reconstruction of Single Image Super Resolution [J].Acta Optica Sinica,2018,38(9):130-136.
[15] QING X J,SHAN Y Y,XIAO J J,et al.Self-Learning Single Image Super-resolution Reconstruction Based on Compressive Sensing and SVR [J].Computer Science,2017,44(11):169-174.
[16] HUANG J B,SINGH A,AHUJA N.Single image super-resolution from transformed self-exemplars[C]//Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2015:5197-5206.
[17] 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.
[18] DONG C,LOY C C.Accelerating the super-resolution convolutional neural network[C]//European Conference on Computer Vision (ECCV).IEEE,2016:391-407.
[19] BEE L,SANGHYUM S,HEEWON K,et al.Enhanced deep residual networks for single image super-resolution[C]//Compu-ter Vision and Pattern Recognition Workshop (CVPRW).IEEE,2017:136-144.
[20] LI J,FANG F,MEI K,et al.Multi-scale residual network for image super-resolution[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:517-532.
[21] LIAN Y Y,WU X J.Research on Image Super-Resolution Reconstruction of Super Deep Convolutional Neural Network[J].Computer Engineering,2019,45(1):217-220.
[1] ZHANG Fan, HE Wen-qi, JI Hong-bing, LI Dan-ping, WANG Lei. Multi-view Dictionary-pair Learning Based on Block-diagonal Representation [J]. Computer Science, 2021, 48(1): 233-240.
[2] CHENG Zhong-Jian, ZHOU Shuang-e and LI Kang. Sparse Representation Target Tracking Algorithm Based on Multi-scale Adaptive Weight [J]. Computer Science, 2020, 47(6A): 181-186.
[3] WANG Jun-qian, ZHENG Wen-xian, XU Yong. Novel Image Classification Based on Test Sample Error Reconstruction Collaborative Representation [J]. Computer Science, 2020, 47(6): 104-113.
[4] WU Qing-hong, GAO Xiao-dong. Face Recognition in Non-ideal Environment Based on Sparse Representation and Support Vector Machine [J]. Computer Science, 2020, 47(6): 121-125.
[5] WANG Jun-hao, YAN De-qin, LIU De-shan, XING Yu-jia. Algorithm with Discriminative Analysis Dictionary Learning by Fusing Extreme Learning Machine [J]. Computer Science, 2020, 47(5): 137-143.
[6] QIAN Ling-long, WU Jiao, WANG Ren-feng, LU Hui-juan. Multi-document Automatic Summarization Based on Sparse Representation [J]. Computer Science, 2020, 47(11A): 97-105.
[7] LI Xiao-yu,GAO Qing-wei,LU Yi-xiang,SUN Dong. Image Fusion Method Based on Image Energy Adjustment [J]. Computer Science, 2020, 47(1): 153-158.
[8] LI Gui-hui,LI Jin-jiang,FAN Hui. Image Denoising Algorithm Based on Adaptive Matching Pursuit [J]. Computer Science, 2020, 47(1): 176-185.
[9] ZHANG Bing, XIE Cong-hua, LIU Zhe. Multi-focus Image Fusion Based on Latent Sparse Representation and Neighborhood Information [J]. Computer Science, 2019, 46(9): 254-258.
[10] WANG Shu-yun, GAN Zong-liang, LIU Feng. Face Hallucination Reconstruction Algorithm Based on Hierarchical Clustering Regression Model [J]. Computer Science, 2019, 46(8): 298-302.
[11] SONG Xiao-xiang,GUO Yan,LI Ning,YU Dong-ping. Missing Data Prediction Algorithm Based on Sparse Bayesian Learning in Coevolving Time Series [J]. Computer Science, 2019, 46(7): 217-223.
[12] ZHANG Fu-wang, YUAN Hui-juan. Image Super-resolution Reconstruction Algorithm with Adaptive Sparse Representationand Non-local Self-similarity [J]. Computer Science, 2019, 46(6A): 188-191.
[13] HUA Zhen, ZHANG Hai-cheng, LI Jin-jiang. End-to-end Image Super Resolution Based on Residuals [J]. Computer Science, 2019, 46(6): 246-255.
[14] DU Xiu-li, ZUO Si-ming, QIU Shao-ming. Adaptive Dictionary Learning Algorithm Based on Image Gray Entropy [J]. Computer Science, 2019, 46(5): 266-271.
[15] RU Feng, XU Jin, CHANG Qi, KAN Dan-hui. High Order Statistics Structured Sparse Algorithm for Image Genetic Association Analysis [J]. Computer Science, 2019, 46(4): 66-72.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[3] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[4] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[5] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .
[6] YANG Yu-qi, ZHANG Guo-an and JIN Xi-long. Dual-cluster-head Routing Protocol Based on Vehicle Density in VANETs[J]. Computer Science, 2018, 45(4): 126 -130 .
[7] HAN Kui-kui, XIE Zai-peng and LV Xin. Fog Computing Task Scheduling Strategy Based on Improved Genetic Algorithm[J]. Computer Science, 2018, 45(4): 137 -142 .
[8] PANG Bo, JIN Qian-kun, HENIGULI·Wu Mai Er and QI Xing-bin. Routing Scheme Based on Network Slicing and ILP Model in SDN[J]. Computer Science, 2018, 45(4): 143 -147 .
[9] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151 .
[10] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .