Learning human preferences to sharpen images

Myra Nam, Narendra Ahuja

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


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
Number of pages4
StatePublished - 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: Nov 11 2012Nov 15 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other21st International Conference on Pattern Recognition, ICPR 2012

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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