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
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