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Title:Automated image sharpening via supervised learning with human preferences
Author(s):Nam, Myra
Director of Research:Ahuja, Narendra
Doctoral Committee Chair(s):Ahuja, Narendra
Doctoral Committee Member(s):Huang, Thomas S.; Liang, Zhi-Pei; Bhargava, Rohit
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Image sharpening
Image Enhancement
human visual perception
perceptual preference
Gaussian mixture model
Abstract:We propose an automatic method for image sharpening that maximizes the perceptual sharpness while preserving naturalness and original colors of a given image. We hypothesize a set of image properties to model the context for selection of sharpening parameters. We hypothesize and then verify that these properties contain the unknown feature (sub)space that could uniquely determine the best sharpening parameters. The (sub)space is learned through a training set of examples for which human preferences are obtained in psychophysical experiments. The human judgments are also used to learn the function that maps the (sub)space to the best sharpening parameter values. This function thus facilitates adaptive enhancement across an image since only the local image properties determine the value the function takes. Experimental results demonstrate the adaptive nature and superior performance of our approach over other algorithms. In addition, we present spatial approaches of respectively measuring the edge sharpness strength and the perceptual sharp- ness preferences, which do not require a reference image. The proposed approaches quantify the perceptual visual quality that reflects the responses of the human visual perception.
Issue Date:2012-09-18
Rights Information:Copyright 2012 Myra Nam
Date Available in IDEALS:2012-09-18
Date Deposited:2012-08

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