Improved Feature Selection for Neighbor Embedding Super-Resolution Using Zernike Moments

  • Deepasikha MishraEmail author
  • Banshidhar Majhi
  • Pankaj Kumar Sa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)


This paper presents a new feature selection method for learning based single image super-resolution (SR). The performance of learning based SR strongly depends on the quality of the feature. Better features produce better co-occurrence relationship between low-resolution (LR) and high-resolution (HR) patches, which share the same local geometry in the manifold. In this paper, Zernike moment is used for feature selection. To generate a better feature vector, the luminance norm with three Zernike moments are considered, which preserves the global structure. Additionally, a global neighborhood selection method is used to overcome the problem of blurring effect due to over-fitting and under-fitting during K-nearest neighbor (KNN) search. Experimental analysis shows that the proposed scheme yields better recovery quality during HR reconstruction.


Super-resolution Zernike moment Luminance norm Manifold learning Global neighborhood selection Locally linear embedding 


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Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Deepasikha Mishra
    • 1
    Email author
  • Banshidhar Majhi
    • 1
  • Pankaj Kumar Sa
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyRourkelaIndia

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