An efficient selective perceptual-based super-resolution estimator

Lina Karam, Nabil G. Sadaka, Rony Ferzli, Zoran A. Ivanovski

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

In this paper, a selective perceptual-based (SELP) framework is presented to reduce the complexity of popular super-resolution (SR) algorithms while maintaining the desired quality of the enhanced images/video. A perceptual human visual system model is proposed to compute local contrast sensitivity thresholds. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. Processing only a set of perceptually significant pixels reduces significantly the computational complexity of SR algorithms without losing the achievable visual quality. The proposed SELP framework is integrated into a maximum-a posteriori-based SR algorithm as well as a fast two-stage fusion-restoration SR estimator. Simulation results show a significant reduction on average in computational complexity with comparable signal-to-noise ratio gains and visual quality.

Original languageEnglish (US)
Article number5873150
Pages (from-to)3470-3482
Number of pages13
JournalIEEE Transactions on Image Processing
Volume20
Issue number12
DOIs
StatePublished - Dec 2011

Keywords

  • Edge detection
  • Human visual system (HVS)
  • Maximum a posteriori (MAP) estimator
  • Maximum-likelihood estimator
  • Perceptual quality
  • Reduced complexity
  • Super-resolution (SR)

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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