Zernike-Moment-Based Image Super Resolution

  • Authors:

  • Xinbo Gao;Qian Wang;Xuelong Li;Dacheng Tao;Kaibing Zhang

  • Affiliations:

  • School of Electronic Engineering, Xidian University, Xi'an, Shaanxi Province, China;School of Electronic Engineering, Xidian University, Xi'an, Shaanxi Province, China;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences, X ...;Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney,;School of Electronic Engineering, Xidian University,

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2011

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Abstract

Multiframe super-resolution (SR) reconstruction aims to produce a high-resolution (HR) image using a set of low-resolution (LR) images. In the process of reconstruction, fuzzy registration usually plays a critical role. It mainly focuses on the correlation between pixels of the candidate and the reference images to reconstruct each pixel by averaging all its neighboring pixels. Therefore, the fuzzy-registration-based SR performs well and has been widely applied in practice. However, if some objects appear or disappear among LR images or different angle rotations exist among them, the correlation between corresponding pixels becomes weak. Thus, it will be difficult to use LR images effectively in the process of SR reconstruction. Moreover, if the LR images are noised, the reconstruction quality will be affected seriously. To address or at least reduce these problems, this paper presents a novel SR method based on the Zernike moment, to make the most of possible details in each LR image for high-quality SR reconstruction. Experimental results show that the proposed method outperforms existing methods in terms of robustness and visual effects.