2026-09-21T19:21:06Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/34224 2023-07-10 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Ahuja, Narendra Ahuja, Narendra Huang, Thomas S. Liang, Zhi-Pei Bhargava, Rohit Nam, Myra 2012-09-18T21:06:39Z 2012-09-18T21:06:39Z 2012-08 2012-09-18T21:06:39Z 2012-08 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2012-07-05T20:18:53Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 ecethesis.tex: 7006 bytes, checksum: 8d9f53e66e243e236c8b31f9ea26ff5f (MD5) Nam_Myra.pdf: 31360782 bytes, checksum: 4e9cecd5c8fd1f9108c969dcc1beb59a (MD5) Made available in DSpace on 2012-09-18T21:06:39Z (GMT). No. of bitstreams: 3 Nam_Myra.pdf: 31360782 bytes, checksum: 4e9cecd5c8fd1f9108c969dcc1beb59a (MD5) ecethesis.tex: 7006 bytes, checksum: 8d9f53e66e243e236c8b31f9ea26ff5f (MD5) license.txt: 4058 bytes, checksum: cbfbc9953eca13ea65a0599c2075fefc (MD5) http://hdl.handle.net/2142/34224 en Copyright 2012 Myra Nam Image sharpening Image Enhancement human visual perception perceptual preference Gaussian mixture model Automated image sharpening via supervised learning with human preferences Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Electr & Computer Eng-UIUC 10KS1200PHD