Learning human preferences to sharpen images

Myra Nam, Narendra Ahuja

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

Abstract

We propose an image sharpening method that automatically optimizes the perceived sharpness of an image. Image sharpness is defined in terms of the one-dimensional contrast across region boundaries. Regions are automatically extracted for all natural scales present that are themselves identified automatically. Human judgments are collected and used to learn a function that determines the best sharpening parameter values at an image location as a function of certain local image properties. We use the Gaussian mixture model (GMM) to estimate the joint probability density of the preferred sharpening parameters and local image properties. The latter are then adaptively estimated by parametric regression from GMM. Experimental results demonstrate the adaptive nature and superior performance of our approach over the traditional Unsharp Masking method.

Original languageEnglish (US)
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages2173-2176
Number of pages4
Publication statusPublished - 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: Nov 11 2012Nov 15 2012

Other

Other21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period11/11/1211/15/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Nam, M., & Ahuja, N. (2012). Learning human preferences to sharpen images. In ICPR 2012 - 21st International Conference on Pattern Recognition (pp. 2173-2176). [6460593]