Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (3): 471-482.doi: 10.21629/JSEE.2018.03.04

• Electronics Technology • Previous Articles     Next Articles

Fast image super-resolution algorithm based on multi-resolution dictionary learning and sparse representation

Wei ZHAO1,*(), Xiaofeng BIAN1(), Fang HUANG1(), Jun WANG1(), Mongi A ABIDI2()   

  1. 1 School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
    2 Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville TN 37996, USA
  • Received:2017-01-24 Online:2018-06-28 Published:2018-07-02
  • Contact: Wei ZHAO;;;;
  • About author:ZHAO Wei was born in 1972. She received her B.S., M.S. and Ph.D. degrees, all from School of Automatic Control of Northwestern Polytechnical University, Xi'an, China. Then she did postdoctoral research in Beihang University, and now she is an associate professor there. Her main research interests are digital image processing, automatic target recognition, signal processing in wireless sensor network and information fusion. E-mail:|BIAN Xiaofeng was born in 1993. He received his B.E. degree in Beihang University, China, in 2011. He is currently a master candidate in School of Electronic and Information Engineering of Beihang University, China. His research interest is image processing. E-mail:|HUANG Fang was born in 1991. She received her B.E. degree in Dalian University of Technology, China, in 2010. She is currently a master candidate in School of Electronic and Information Engineering of Beihang University, China. Her research interest is image processing. E-mail:|WANG Jun was born in 1972. He received his B.S. degree from Northwestern Polytechnical University, Xi'an, China, in 1995, and M.S. and Ph.D. degrees from Beihang University, Beijing, China, in 1998 and 2001, respectively. He is currently a professor in School of Electronic and Information Engineering, Beihang University. He is interested in signal processing, DSP/FPGA real-time architecture, target recognition and tracking, and so on. E-mail:|ABIDI Mongi A. was born in 1955. He holds the life-time Cooke-Eversole professorship in the College of Engineering at the University of Tennessee where he served as a faculty since 1987. He graduated over 75 Masters and Ph.D. students and managed over million of external funding in the areas of image processing and robotics. He has published over 320 papers and is co-author or co-editor of four books. His main research interests are data fusion in robotics and machine intelligence, 3D imaging, face identification, and color image processing and applications. E-mail:


Sparse representation has attracted extensive attention and performed well on image super-resolution (SR) in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning (MRDL) model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method (APPM). The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches. Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.

Key words: single image super-resolution (SR), sparse representation, multi-resolution dictionary learning (MRDL), adaptive patch partition method (APPM)