The contrast enhancement with mean and variance
thansformation has following advantages: 1. It is a
linear transformation. 2. The restraint role of a few
limit grey levels can be eliminated so that the con-
trast enhance effect can be improved as a whole.
3. The transformation effect depends on the selec-
tion of new mean and variance mainly, and the rela-
tion between the new/old mean and variance is
invariant basically, therefore the kind of images can
be processed effectively if only the regularity men-
tioned above has been found.
ESTABLISHMENT AND MODIFICATION
OF A RULE BASE
The function of rules is to acquire the expected
new mean and variance on the basis of the image
fact bases. The rules are invariant with input
image, i. e., independent of input image. The
establishment and modification of rules are based on
man' s knowledge and the analysis of processing re-
sults for various images.
As is well known, if the mean of a image with
grey range[0,255] is nearly equal to the center value
128 and the variance is large, the image must be
very clear. Therefore, it is unnecessary for the image
to be enhanced, i.e,the mean and variance of the
image are kept unvaried. Otherwise, the mean of a
source image should be transformed nearly to 128,
and the variance be enlarged. Based on the above
idea, a rule base can be established.
The mean and variance range is divided into 7 re-
gions respectively, which are expressed with
semantics as shown in Fig.2. Then, 49 rules in all
are generated based on the different combination of
the means and variances. The format of a rule is
^IF-" THEN -- "
enhancement rule 10:
IF: (1) Ms is vs, (2) Vs is s
THEN: (1) M,=M;:1.5+30, (2) V,= V, 3 + 1500
The semantics of vs and s are shown in Fig. 2.
Now, the frame of a rule base has been formed.
For example,
195 225
0 30 60 90 165 255
! es el |
E t
S —
m
(a) Semantic Definitions of Mean
L 1 1 i 1 4 i 4
0 100 1000 2000 3000 6000 8000 10000
— pw
+=" br
m E
Fig .2 (b) Semantic Definition of Variance
In practice, the best enhancement effect may not
be sometimes achieved by the mean and variance
666
transformation based on the rules above. Therefore,
it is necessary to modify the rules according to the
analysis of image processing results.
In the rule base, the transformation relation be-
tween the mean and variance of output image and
those of source image is
Mo = Ms.a + C1 | (7)
Vo = Vs-b+Ca |
where C,, C, are constants, a, b are modifiable
parameters. The procedure of solving the modifiable
parameters according to the best result acquired by
interactive method is given as follows:
(1) Apply the mean and variance transformation
to an image based on the initial
(2) Display the enhanced image and the curve of
Fig.l (including the values of Isi and Is2)
(3) Answer the question: "Are you satisfied with
the enhanced image? " If yes, goto(8) Otherwise,
(4) Indication: "If to increase (or decrease) the satu-
ration of low grey levels, increase (or decrease) Is: .
If to increase(or decrease) the saturation of high
grey levels, decrease(or increase) Is2. Input Is: and
rule base.
Is2 .”
(S) Solve Mo and Vo with Isi and Is2 input:
) :
Mo7(123—) Ms-Isi) |
Isz—Isi | (8)
er 255-2 (
Net ioi vs |
(6) Apply the mean and variance transformation
based on the modified Mo and Vo.
(7) Goto (2)
(8) Answer the question: " Modify the rule base or
not?" If no, goto (9). Otherwise, modify the
parameters a and b of corresponding rule in rule
base as follows:
aimi z25- Mrs e 3
p 5 2 © [
Is2— Isi Vs |
(9) End.
RULE-BASED LOCALIZED IMAGE
ENHANCEMENT TECHNIQUE
When an input image has strong spatially depen-
dent variation in scene illumination, the mean and
variance transformation illustrated above based on
the global information over an entire image can not
obtain high-quality output images. For this reason,
a powerful and elegant localized image enhancement
technique is presented in this paper.
The scheme of the localized image enhancement
technique is illustrated as follows: (1) Divide an
input image into several block subimages, e. g., 25
block subimages as shown in Fig.3(c) (2)Apply the