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Visual-Quality Optimizing Super Resolution

Feng Liu, Jinjun Wang, Shenghuo Zhu, Michael Gleicher, Yihong Gong
Computer Graphics Forum, Volume 28, Number 1, page 127--140 — 2009
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    In this paper, we propose a robust image super-resolution algorithm, which aims to maximize the overall visual quality of super-resolution results. We consider a good super-resolution algorithm to be fidelity preserving, image detail enhancing and smooth. Accordingly, we define perception-based measures for these visual qualities. Based on these quality measures, we formulate image super-resolution as an optimization problem aiming to maximize the overall quality. Since the quality measures are quadratic, the optimization can be solved efficiently. Experiments on a large image set and subjective user study demonstrate the effectiveness of the perception-based quality measures and the robustness and efficiency of the presented method.

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      author       = "Liu, Feng and Wang, Jinjun and Zhu, Shenghuo and Gleicher, Michael and Gong, Yihong",
      title        = "Visual-Quality Optimizing Super Resolution",
      journal      = "Computer Graphics Forum",
      number       = "1",
      volume       = "28",
      pages        = "127--140",
      year         = "2009",
      url          = "http://graphics.cs.wisc.edu/Papers/2009/LWZGG09"

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