08-06-2012, 03:04 PM
Histogram Specification: A Fast and Flexible Method
to Process Digital Images
Histogram Specification.pdf (Size: 2.46 MB / Downloads: 13)
Abstract
Histogram specification has been successfully used in
digital image processing over the years. Mainly used as an image
enhancement technique, methods such as histogram equalization
(HE) can yield good contrast with almost no effort in terms of
inputs to the algorithm or the computational time required. More
elaborate histograms can take on problems faced by HE at the
expense of having to define the final histograms in innovative ways
that may require some extra processing time but are nevertheless
fast enough to be considered for real-time applications.
INTRODUCTION
WHAT CONSTITUTES good contrast? That is a good
question since image quality is very subjective and,
as the phrase says, beauty is in the eyes of the beholder.
It depends on the user, and we would like to add here that
it also depends on the scene itself. Back in the days of
darkroom developing—we assume those days are almost over
for the photography enthusiast—developing black-and-white
pictures required a test strip, which was made by exposing
the photographic paper to different exposure times, as shown
in Fig. 1.
HS TECHNIQUES FOR CONTRAST ENHANCEMENT
Brightness-Preserving HE
It is well known that, if the histogram of an image shows a
strong peak because the image is dominated by a large area of a
single gray-level value, this can cause problems when using HE
[3]. Fig. 8 shows an example when HE can cause bad results.
Regardless of the shape of the histogram in the original image,
the HE will yield an image with a final level of brightness
that is close to 0.5. For the example in Fig. 8, the mean of the
histogram-equalized image is μHE = 0.4991. There is nothing
wrong with that, you may say, “since the over enhancement
seems to be caused by the spreading, not the average brightness.”
Yes but not entirely true is the answer. Recall the scenario
of a picture of a polar bear in a winter tundra scenery. The image
should be very white, and if the HE is applied, the polar bear
may look more like a brown bear in the end. Something similar
has happened to Fig. 8. The original mean is μo = 0.2854, and
it can be seen that the HE produced a brighter result in that case,
way too bright in some areas, particularly the face of the woman
sitting in the front.
CONCLUSION
The image enhancement method presented in this paper has
run in less than 2 s using a 2-GHz personal computer and has
produced satisfactorily enhanced images that yielded bad results
using the HE. By combining and improving two different
ways to enhance the contrast, our method has compared well
with respect to each approach and has solved some of the issues
of the original techniques. The method has also shown good
results when used in color images. Additionally, the HS has
been proposed as a way to improve image segmentation.