-B3, 2012
egistration using
[mage Processing
lomized trees for
iference on Com-
>—781.
m scale-invariant
ter Vision 60(2),
., 2005. Random
In: IEEE Confer-
ition, pp. 34-40.
"cognition with a
aputer Vision and
Processing: The
<ubik, K., 2007.
scanner data and
on and sensitivity
ind Remote Sens-
08. Hierarchical
to combine local
ice on Computer
3. Semantic tex-
gmentation. In:
Pattern Recogni-
si, A., 2006. Tex-
ext modeling for
on. In: European
-15.
‚A. and Freeman,
'ations in images.
ter Vision, pp. 1-
ragios, N., 2010.
edural shape pri-
ision and Pattern
C. G. M., 2010.
cene recognition.
Machine Intelli-
Vifeat: An
sion algorithms.
lassification with
ference on Com-
3.
in digital spaces:
imulations. IEEE
hine Intelligence
e classification of
c Image Analysis
220.
RESEARCH ON THE IMPROVED IMAGE DODGING ALGORITHM BASED ON MASK
TECHNIQUE
Fang Yao* ,Han Hu", Youchuan Wan*
"School of Remote Sensing Information and Engineering, Wuhan University, Luoyu Road, Wuhan, China
KEY WORDS: Remote sensing images, dodging, Mask, image quality, assessment.
ABSTRACT:
The remote sensing image dodging algorithm based on Mask technique 1s a good method for removing the uneven lightness within a
single image. However, there are some problems with this algorithm, such as how to set an appropriate filter size, for which there is
no good solution. In order to solve these problems, an improved algorithm is proposed. In this improved algorithm, the original
image is divided into blocks, and then the image blocks with different definitions are smoothed using the low-pass filters with
different cut-off frequencies to get the background image; for the image after subtraction, the regions with different lightness are
processed using different linear transformation models. The improved algorithm can get a better dodging result than the original one,
and can make the contrast of the whole image more consistent.
1. INTRODUCTION
In the process of acquiring remote sensing images, the effect
of internal and external factors will lead to the differences of
hue, lightness, contrast, etc. inside a single image. The
differences are mainly caused by imaging non-uniformity of
optical lens, atmospheric attenuation, clouds, smog, different
light conditions due to sun/shade conditions, and so on,
which directly degrades remote sensing images and affects
their subsequent applications and post-processing(such as
feature extraction, target recognition, classification,
interpretation, etc.) (Li et al, 2006). Thus, dodging
(removing the differences of hue, lightness and contrast) has
become an important issue in remote sensing application.
At present the algorithms of image dodging mainly focus on
how to acquire the trend of lightness in an image to
compensate for the lightness of different regions. According
to the different methods of how to get the lightness trend in
an image, the dodging algorithms can be divided into the
following categories (Russ, 1999):
(1) Background fitting: select some background points
automatically or by human-machine interaction, then utilize a
mathematical model to fit the gray values to a function of x
and y, B(x, y) as the estimate of the background image, and
finally subtract the background image from the original
image(Blohm, 1997; Jin et al., 2000). The adaptive dodging
method Zhang et al. (2003) and Li (2005) proposed also
belongs to this category. However, because the reasons for
causing the uneven distribution of lightness are varied and
the distribution of ground objects themselves is irregular, it is
difficult to choose the most appropriate model using this
method. Chandelier (2009) proposed a parametric, semi-
empirical radiometric model, and its principle is quite similar
to the standard aerial triangulation. The results are
satisfactory when atmospheric conditions are favorable and
stable.
(2) Sorting correction: Assuming that the background is
always darker than the target in any region of an image,
substitute the minimum gray value in the neighborhood for
the gray value of the pixel. Repeat the above-mentioned
process several times to obtain the background image, and
then subtract the background image from the original
image(Zhen et al., 2003). This method can correct the image
with the quite uneven lightness, but the computing speed is
slow. In order to get a good result, it is necessary to smooth
the complicated background image. However, the process is
cumbersome(Li, 1995).
(3) Filtering in frequency domain: Assuming that the
lightness values of the background image are a series of low-
frequency signals, adopt a low pass filtering method to get
the background image. Wang et al. (2004) proposed a
dodging algorithm based on MASK technique. They utilized
the Gaussian low pass filter to acquire the background image,
then subtracted the background image from the original
image, and finally stretch the result image to increase the
contrast. The dodging algorithm Hu et al. (2004) proposed is
also based on Mask technology, and only the methods of
producing the background image and increasing the contrast
are different from Wang et al. (2004). Sun (2008) divided the
background image by the original image after obtaining the
lightness distribution of the background image. This can
decrease the contrast of the brighter regions and increase the
contrast of the darker regions, and finally get a relatively
even contrast.
Because the background image only reflects the lightness
distribution of the original image, rather than the detailed
information, the detailed information must be remove from
the background image as much as possible. Considering that
ground objects and image definitions are different in different
regions of an image, the background image should be
acquired using different parameters. Currently, most
researchers implement dodging processing for different
regions within an image using the same method with the
same parameters. Aiming at the problem above, this paper
puts forward an improved image dodging algorithm based on
Mask technique.
2. THE PRINCIPLE OF MASK DODGING
The Mask dodging technique originated from the photoprint
