First-order derivative-based super-resolution

Muhammad Haris, Muhammad Rahmat Widyanto, Hajime Nobuhara

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


A single fast super-resolution method based on first-order derivatives from neighbor pixels is proposed. The basic idea of the proposed method is to exploit a first-order derivatives component of six edge directions around a missing pixel, followed by back projection to reduce noise estimated by the difference between simulated and observed images. Using first-order derivatives as a feature, the proposed method is expected to have low computational complexity, and it can theoretically reduce blur, blocking, and ringing artifacts in edge areas compared to previous methods. Experiments were conducted using 900 natural grayscale images from the USC-SIPI Database. We evaluated the proposed and previous methods using peak signal-to-noise ratio, structural similarity, feature similarity, and computation time. Experimental results indicate that the proposed method clearly outperforms other state-of-the-art algorithms such as fast curvature-based interpolation.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalSignal, Image and Video Processing
Issue number1
Publication statusPublished - 1 Jan 2017


  • Back projection
  • Edge direction
  • First derivatives
  • Interpolation
  • Super-resolution


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