Jinhui Qin, September 2000--October 2001, "A New
Wavelet-based Method For Contrast/Edge Enhancement", degree
conferred on Spring 2002 convocation, Computer Science
Department, University of Western Ontario, Canada.
M.Sc. Thesis Abstract
Digital images are often deteriorated by noises due to
various sources of interference and other phenomena that may
affect the quality of these images. Image enhancement is a
mathematical technique that is aimed at realizing improvement
in the quality of a given image. In this research we focus
only on certain image enhancement issues, namely, contrast
enhancement and edge enhancement.
Contrast enhancement is usually achieved by histogram
equalization in the spatial domain to redistribute gray
levels uniformly. However, it has a drawback, which is some
information might be lost. Meanwhile, edge enhancement
attempts to emphasize the fine details in the original image.
But in spatial domain it is hard to selectively enhance
details at different scales. Moreover, in the spatial domain,
applying contrast and edge enhancement techniques in
different orders may yield different enhancement results.
Wavelet-based image analysis provides multiple
representations of a single image. It decomposes an image
into approximated-coefficients and multi-resolution
detailed-coefficients. To overcome the above spatial domain
enhancement issues, a new wavelet-based image enhancement
method is proposed. The proposed method histogram-equalizes
the approximated-coefficients. At the same time, it
high-boost filters the detailed-coefficients at selected
resolution levels separately. Since contrast and edge
enhancements are applied on different components in the
wavelet domain, the contrast and edge enhancements should not
affect each other. Moreover, the order of applying both of
them becomes irrelevant.
The proposed method is implemented in C++ on UNIX platform.
Various parameters in the method are adjusted and discussed
based on experiments. These parameters include number of
transformation levels, different wavelet filter set
selections, the stretching factor of coefficients in a
component over which the histogram equalization is performed
and the A value of the high-boost filtering function. Final
experiments show that utilizing the proposed method can
achieve robust contrast and edge enhancement.