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Super-resolution: a comprehensive survey

Abstract

Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real-world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

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Acknowledgments

The authors would like to express their thanks to Professor Peyman Milanfar from Multi-Dimensional Signal Processing group at University of California Santa Cruz for reading the paper and providing us with his thoughtful comments and feedbacks. We are really thankful for the interesting comments of anonymous reviewers which have indeed helped improving this paper.

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Nasrollahi, K., Moeslund, T.B. Super-resolution: a comprehensive survey. Machine Vision and Applications 25, 1423–1468 (2014). https://doi.org/10.1007/s00138-014-0623-4

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Keywords

  • Super-resolution
  • Hallucination
  • Reconstruction
  • Regularization