An efficient, selective, perceptual-based super-resolution estimator

Rony Ferzli, Zoran A. Ivanovski, Lina Karam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In this paper, a SELective Perceptual-based (SELP) scheme 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 (HVS) model is proposed to compute the contrast sensitivity threshold for a given background intensity. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. This is accomplished by estimating the contrast sensitivity threshold locally over a block. Next, the absolute difference between each pixel and its neighbors is computed and compared to the threshold upon which a decision is made to include the pixel in the SR estimator for the next iteration or not. The perceptual model is integrated into a MAP-based SR algorithm as well as a fast ML estimator. Simulation results show up to 47% reduction on average in computational complexity with comparable SNR gains and visual quality.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Pages1260-1263
Number of pages4
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States
Duration: Oct 12 2008Oct 15 2008

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2008 IEEE International Conference on Image Processing, ICIP 2008
CountryUnited States
CitySan Diego, CA
Period10/12/0810/15/08

Keywords

  • ML estimator
  • Map
  • Perceptual quality
  • Reduced complexity
  • Super-resolution

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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