Middle-frequency based refinement for image super-resolution

Jae Hee Jun, Ji Hoon Choi, Jong Ok Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This letter proposes a novel post-processing method for self-similarity based super-resolution (SR). Existing back-projection (BP) methods enhance SR images by refining the reconstructed coarse highfrequency (HF) information. However, it causes artifacts due to interpolation and excessively smoothes small HF signals, particularly in texture regions. Motivated by these observations, we propose a novel postprocessing method referred to as middle-frequency (MF) based refinement. The proposed method refines the reconstructed HF information in the MF domain rather than in the spatial domain, as in BP. In addition, it does not require an internal interpolation process, so it is free from the side-effects of interpolation. Experimental results show that the proposed algorithm provides superior performance in terms of both the quantity of reproduced HF information and the visual quality.

Original languageEnglish
Pages (from-to)300-304
Number of pages5
JournalIEICE Transactions on Information and Systems
VolumeE99D
Issue number1
DOIs
Publication statusPublished - 2016 Jan

Keywords

  • Back-projection
  • Middle-frequency
  • Post processing
  • Superresolution

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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