Proceedings of Topical Meeting on Optoinformatics (pdf-format, 1.21 ...

SAINT-PETERSBURG, October 17 – 20, 2005 17
IMAGE ENHANCEMENT BY IMPROVED CONTRAST-
STRETCHING TECHNIQUE
Ching-Chung Yang, Department <strong>of</strong> Electronic Engineering, Far East College,
49 Chung Hua Road, Hsin-Shih, Tainan, Taiwan, R. O. C.
E-mail: yang10.cc@msa.hinet.net
We demonstrate a modified contrast-stretching method to enhance a nonuniformly
illuminated image. Low-frequency information <strong>of</strong> the image is still
processed by the conventional technique, while the high-frequency information
is exaggerated by the log transformation. The final image improves the contrast
to a better extent.
This article demonstrates a brand new approach to sharpening a non-uniformly
illuminated image. The proposed method at first separates the original image matrix to two
sub-matrices representing the high and low frequency information. The low frequency submatrix
is then processed by the contrast-stretching manipulation, while the high frequency
sub-matrix processed by the log transformation. At last we reconstruct these two new submatrices
to derive the enhanced final image.
The element in the high frequency sub-matrix is represented by (I 1 -I 2 )/2, and that <strong>of</strong>
the low frequency is (I 1 +I 2 )/2. Where I 1 I 2 are any two near-by pixels among the original
image matrix.
In this study, the low frequency information is processed by the conventional rubberband
transformation to raise the image’s contrast. In the meantime, the near-by pixels’
visibility [(I 1 -I 2 )/(I 1 +I 2 )] is also improved for that the high frequency information is
simultaneously enhanced by the log transformation. Thus the original image could be
further sharpened on the bases <strong>of</strong> the traditional algorithm.
We illustrate our method by employing a temple’s image that is partially shaded
indoors as shown in Figure 1(a). The original image looks unclear in the shaded area owing
to the lack <strong>of</strong> illumination. Our method could sharpen the whole image to a better extent in
comparison with the conventional contrast-stretching approach. This is shown in Figure
1(b)1(c).
To quantitatively evaluate our method, we compare the histograms <strong>of</strong> the processed
images by Matlab 6.0. It is clear that the original picture gathers its pixels in lower graylevels
as shown in Figure 2(a). Although the conventional method shifts these pixels
toward the higher gray-level as shown in Figure 2(b), the discontinuity happening in the
lower gray-level region somehow degrades its own contrast. While by using our method,
the final image continuously spreads its pixels from lower to higher gray-levels without
any discontinuity as shown in Figure 2(c). This would help to increase the overall contrast
<strong>of</strong> the original image.
We also calculate the statistical data including the mean value, standard deviation,
minimum, and maximum. The calculated mean value implies that our method is essentially
based on the conventional contrast-stretching approach. While by comparison with the
standard deviations, it is obvious that our image has better performance in the contrast
property. This is also shown in Table 1.