Data-driven Automatic Edge Sharpening

Nam, Myra

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https://hdl.handle.net/2142/14685 Copy

Description

Title

Data-driven Automatic Edge Sharpening

Author(s)

Nam, Myra

Issue Date

2010-01-06T16:21:15Z

Director of Research (if dissertation) or Advisor (if thesis)

Ahuja, Narendra

Doctoral Committee Chair(s)

Ahuja, Narendra

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• edge-sharpening
• cumulative logistic regression
• Image Enhancement
• Perception

Abstract

Sharpening is a powerful image transformation because sharp edges can bring out image details. Sharpness is achieved by increasing local contrast and reducing edge widths. We present a method that enhances sharpness of images and thereby their perceptual quality. Most existing enhancement techniques require user input to improve the perception of the scene in a manner most pleasing to the particular user. Our goal of image enhancement is to improve the perception of sharpness in digital images for human viewers. We consider two parameters in order to exaggerate the differences between local intensities. The two parameters exploit local contrast and widths of edges. We start from the assumption that color, texture, or objects of focus such as faces affect the human perception of photographs. When human raters are presented with a collection of images with different sharpness and asked to rank them according to perceived sharpness, the results have shown that there is a statistical consensus among the raters. We introduce a ramp enhancement technique by modifying the optimal overshoot in the ramp for different region contrasts as well as the new ramp width. Optimal parameter values are searched to be applied to regions under the criteria mentioned above. In this way, we aim to enhance digital images automatically to create pleasing image output for common users.

Graduation Semester

2009-12

Permalink

http://hdl.handle.net/2142/14685

Copyright and License Information

Copyright 2009 Myra Nam

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