A multi-frame image super-resolution method

  • Authors:

  • Xuelong Li;Yanting Hu;Xinbo Gao;Dacheng Tao;Beijia Ning

  • Affiliations:

  • State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shaanxi, PR China;School of Medical Technology and Engineering, Xinjiang Medical University, Xinjiang 830001, PR China;School of Electronic Engineering, Xidian University, Xi'an 710071, PR China;School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N4, 639798, Singapore;School of Electronic Engineering, Xidian University, Xi'an 710071, PR China

  • Venue:
  • Signal Processing
  • Year:
  • 2010

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Abstract

Multi-frame image super-resolution (SR) aims to utilize information from a set of low-resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relationship between the recorded LR images to the unknown reconstructed HR image estimates. Recently, regularization-based schemes have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. Working within this promising framework, this paper first proposes two new regularization items, termed as locally adaptive bilateral total variation and consistency of gradients, to keep edges and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thereafter, the combination of the proposed regularization items is superior to existing regularization items because it considers both edges and flat regions while existing ones consider only edges. Thorough experimental results show the effectiveness of the new algorithm for SR reconstruction.