DocumentCode :
595160
Title :
Learning human preferences to sharpen images
Author :
Nam, Minho ; Ahuja, Narendra
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
2173
Lastpage :
2176
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.
Keywords :
Gaussian processes; adaptive estimation; feature extraction; image enhancement; image segmentation; probability; regression analysis; GMM; Gaussian mixture model; automatic image perceived sharpness optimization; automatic region boundary extraction; human judgment collection; human preference learning; image location; image sharpening method; joint adaptive probability density estimation; local image properties; natural scales; one-dimensional image contrast; parametric regression; sharpening parameter values; Feature extraction; Humans; Image color analysis; Image edge detection; Image segmentation; Noise; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460593
Link To Document :
بازگشت