Signal, Image and Video Processing

, Volume 11, Issue 5, pp 961–968 | Cite as

A fast single-image super-resolution via directional edge-guided regularized extreme learning regression

  • Paheding SidikeEmail author
  • Evan Krieger
  • M. Zahangir Alom
  • Vijayan K. Asari
  • Tarek Taha
Original Paper


The goal of super-resolution (SR) is to increase the spatial resolution of a low-resolution (LR) image by a certain factor using either single or multiple LR input images. This paper presents a machine learning-based approach to reconstruct a high-resolution (HR) image from a single LR image. Inspired by the human visual cortex system, which is sensitive to high-frequency (HF) components in an image, we aim to model this concept by training a neural network to estimate the missing HF components that contain structural details. In our method, various directional edge responses at each pixel are considered to obtain more complete HF information and then a regularized extreme learning regression model is trained using a set of LR and HR images. Finally, the trained system is applied to a LR image to generate HR image. The experimental results confirm the effectiveness and efficiency of the proposed scheme in comparison with the state-of-the-art SR methods.


Super-resolution Directional edges Extreme learning regression Structural similarity 

Supplementary material

11760_2016_1045_MOESM1_ESM.pdf (520 kb)
Supplementary material 1 (pdf 520 KB)


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Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • Paheding Sidike
    • 1
    Email author
  • Evan Krieger
    • 1
  • M. Zahangir Alom
    • 1
  • Vijayan K. Asari
    • 1
  • Tarek Taha
    • 1
  1. 1.Department of Electrical and Computer EngineeringUnierstiy of DaytonDaytonUSA

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