Dehazing for Image and Video Using Guided Filter
Zheqi Lin
School of Information Science & Technology, Sun Yat-sen
University, Guangzhou, 510006, China
Shenzhen Key Laboratory of Digital Living Network and
Content Service
Research Institute of Sun Yat-sen University in Shenzhen,
Shenzhen, 518057,China
Xuansheng Wang
School of Information Science & Technology, Sun Yat-sen
University, Guangzhou, 510006, China
Shenzhen Key Laboratory of Digital Living Network and
Content Service
Research Institute of Sun Yat-sen University in Shenzhen,
Shenzhen, 518057,China
Abstract—Poor visibility in bad weather, such as haze and fog, is a major problem for many applications of computer vision.
Thus, haze removal is highly required for receiving high performance of the vision algorithm. In this paper, we propose a new fast
dehazing method for real-time image and video processing. The transmission map estimated by an improved guided filtering
scheme is smooth and respect with depth information of the underlying image. Results demonstrate that the proposed method
achieves good dehazeing effect as well as real-time performance. The proposed algorithm, due to its speed and ability to improve
visibility, may be used with advantages as pre-processing in many systems ranging from surveillance, intelligent vehicles, to
remote sensing.
Keywords- Image dehazing; dark channel prior; guided filter; down-sampling
1. Introduction
Computer vision system can be used for many ourdoor
applications, such as video surveillance, remote sensing, and
intelligent vehicles. Virtually all computer vision tasks or
computational photography algorithms assume that the input
images are taken in clear weather and robust feature detection
are achieved in high quality image. Unfortunately, this is not
always true in many situations. The quality of a captured image
in bad weather is usually degraded by the presence of haze or
fog in the atmosphere, since the incident light to a camera is
attenuated and the image contrast is reduced. This will become
a major problem in many computer vision applications. The
performance of many vision algorithms such as feature
detection and photometric analysis, will inevitably suffer from
the biased and low-contrast scene radiance.
Dehazing is the process to remove haze effects in captured
images and reconstruct the original colors of natural scenes,
which will be a useful pre-processing for input images and
required for receiving high performance of the vision algorithm.
However, haze removal is a challenging problem since the
degradation is spatial-variant, it depends on the unknown scene
depth. In the literature, a few approaches have been proposed
by using multiple images or additional information. For
example, polarization-based methods [1, 2] remove the haze
effect through two or more images taken with different degrees
of polarization; scene depths are estimated from multiple
images of the same scene that are captured in different weather
conditions when using depth-based methods [3-5]. Although
these methods may produce impressive results, they are
impractical, because the requirements cannot always be
satisfied and make them difficult to meet with real-time
applications of images with changing scenes.
In order to overcome the drawback of multiple images
dehazing algorithms, many researchers focus on achieving haze
removal results from a single degraded image. Tan [6]
developed the contrast maximization technique for haze
removal relied on the observations that images with enhanced
visibility have more contrast than images plagued by bad
weather. Under the assumption that the transmission and the
surface shading are locally uncorrelated, Fattal [7] presented a
method for estimating the transmission in hazy scenes. He et al.
[8] propose a novel prior—dark channel prior—for single
image haze removal, which is based on the statistics of outdoor
haze-free images. Combining a haze imaging model and a soft
matting interpolation method, they can recover a high-quality
haze-free image.
The success of these methods lies on using a stronger prior
or assumption. However, a common drawback of these
methods above is their computational cost and time complexity.
To improve the efficiency of image dehazing, Tarel [9]
proposed a fast dehazing algorithm using a median filter; this
algorithm was very efficient, but as the median filter is not
conformal and edge-preserving, although the received
atmosphere veil are smooth, it does not respect with the depth
information of the scene. Xiao and Gan [10] obtain an initial
atmosphere scattering light through median filtering, then
refine it by guided joint bilateral filtering to generate a new
atmosphere veil which removes the abundant texture
information and recovers the depth edge information.
In this paper, an improved guided filtering scheme is
proposed for the approximation of the transmission map for
real-time image and video dehazing process. The proposed
corresponding author: Zheqi Lin
Open Journal of Applied Sciences
Supplement：2012 world Congress on Engineering and Technology
Cop
y
ri
g
ht © 2012 SciRes.123

method makes good use of the effective image prior—dark
channel prior and guided filter upsampling. An accurate the
transmission map can be estimated effectively. The article is
structured as follows. Section II presents the fog model used.
Section III describes the proposed dehazing algorithm. Section
IV presents simulation results. Finally, Section V concludes the
paper.
2. Haze modeling
The observed brightness of a capture image in the presence
of haze can be modeled based on the atmospheric optics [6, 7,
11] via
()()()(1 ())IxJxtxAtx  (1)
where, I(x) is the observed haze image, J(x) is scene irradiance
(the clear haze-free image), A is the airlight that represents the
ambient light in the atmosphere. t(x)ę[0, 1] is the transmission
of the light reflected by the object, which indicates the depth
information of the scene objects directly. J(x)t(x)on the right-
hand side is called direct attenuation, which describes the scene
radiance and its decay in the medium. The second term A(1-t(x))
is the atmospheric veil (atmospheric scattering light), which
causes fuzzy, color shift, and distortion in the scene. The goal
of haze removal is to recover J(x), A and t (x) from I(x).
3. Single Image Dehazing
In this section, the proposed method will be described in
detail. The rough down-sampled transmission and the airlight
are estimated firstly, then the transmission is smoothed and up-
sampled using a guided filter, and finally the haze-free image is
restored.
A.Extract the Transmission
The core of haze removal for an image is to estimate the
airlight and transmission map. Assuming the airlight is already
known, to recover the hazefree image, the transmission map
should be extracted first. He et al. [8] found that the minimum
intensity in the non-sky patches on hazefree outdoor images
should have a very low value, which is called dark channel
prior. Formally, for an image J, the dark channel value of a
pixel x is defined as:
^`

,, ()
()minmin( ())
dark c
crgb yx
Jx Jy
:
(2)
where, Jc is a color channel of J; ȍ(x) is a patch around x. By
assuming the transmission in a local patch is constant and
taking the min operation to both the patch and three color
channels, the haze imaging model in (1) can be transformed as:
^`
^`
,, ()
,, ()
()
minmin ()
()
()minmin()(1 ())
c
c
crgb yx
c
c
crgb yx
Iy
A
Jy
tx tx
A
:
:
§·
¨¸
©¹
§·

¨¸
©¹

(3)
where, ()tx

is the patch transmission. Since A is always
positive and the dark channel value of a haze-free image J
tends to be zero according to the dark channel prior, we have
^`
,, ()
()
minmin ()0
c
c
crgb yx
Jy
A
:
§·
o
¨¸
©¹
(4)
Then the transmission can be extracted simply by:
^`
,, ()
()
() 1minmin()
c
c
crgb yx
Iy
tx A
:
§·
¨¸
©¹

(5)
Although the dark channel prior is not a good prior for
the sky regions, fortunately, both sky regions and non-sky
regions can be well handled by (5) since the sky is infinitely
distant and its transmission is indeed close to zero. In practice,
the atmosphere is not absolutely free of any particle even in
clear weather. Therefore, a constant parameter Ȧ (0<Ȧ1) is
introduced into(5) to keep a small amount of haze for the
distant objects:
^`
,, ()
()
() 1minmin()
c
c
crgb yx
Iy
tx A Z
:
§·
¨¸
©¹

(6)
The estimated transmission maps using (6) is reasonable.
The main problems are some halos and block artifacts. This is
because the transmission is not always constant in a patch.
Several techniques were proposed to refine the transmission
map, such as soft matting and guided joint bilateral filter.
These techniques were applied on the transmission maps of
the original foggy images and usually several operations
should be used to achieve a good result, which could be
computational intensive. For image haze removal, the time
complexity is a critical problem that needs to be addressed.
High time complexity of dehazing may make the algorithm
impracticable.
B.Refine the Transmission
To improve the efficiency, in the present implementation,
the transmission map is obtained form a down-sampled
minimun channel image. Then, it is refined and up-sampled by
using guided filter, which can be explicitly expressed by [12]:
()
gj
iij
j
tWJt ¦ (7)
124 Cop
y
ri
g
ht © 2012 SciRes.

 
22
:( ,)
11
k
gg
ikjk
g
ij
kij wk
JJ
WJ
w
PP
VH

§·

¨¸ 
¨¸

©¹
¦
(8)
where, Jg is the guidance image; ȝkand ık
2 are the mean and
variance of J
g in wk; |w| is the number of pixels in wk. İis a
regularization parameter.
The refined operation on a down-sampled minimun
channel image leads to a low time complexity and helps to
reduce halos and block artifacts. Joint upsampling using
guided filter is applied to obtain the full transmission map.
The guided filter is reported to be a fast and non-approximate
linear-time algorithm, which can perform as an edge-
preserving smoothing operator like the bilateral filter, but does
not suffer from the gradient reversal artifacts. Moreover, the
guided filter has an O(N) time (in the number of pixels N)
exact algorithm for both gray-scale and color images.
C.Recovering the Scene Radiance
After the transmission map is estimated, the scene
radiance can be recovered according to(1). The term J(x)t(x)
can be very close to zero. When the transmission t(x) is close to
zero, which make the recovered scene radiance J is prone to
noise. Therefore, the transmission t(x) is restricted by a low
bound t0. The final scene radiance J(x) is recovered by
 
 
0
max ,
Ix A
Jx A
tx t


(9)
where, A is the airlight. There are many ways available in
literatures to estimate the airlight [6-9]. For simplicity, A takes
the value of a pixel with highest dark channel value in this
paper.
D.Algorithm process flow
The main process of this algorithm is as follows:
1)Calculate the minimun channel image Imin form I ;
2)Obtain IĻmin by down-sampling Imin;
3)Calcutlate the dark channel IĻdark form IĻmin;
4)Estimate the atmosphere light A;
5)Calculate the patch transmission
t p

;
6)Obtain
f
t p

by refining
t p

using guided filter, guided by
IĻmin;
7)Obtain t by up-sampling
f
t p

using guided filter, guided
by Imin;
8)Get the recovered image.
4. Experimental Results
In this section, the results of the proposed method will be
shown and compared with several state of the art image
dehazing methods in both image restoration quality and the
time complexity. The proposed algorithm is implemented in
the MATLAB language, and a personal computer with a
3.0GHz Core processor is employed in the test.
Fig.1 shows the dehazing process of images with sizes of
1024×768 and 800×600. The patched size is chosen as 11×11
and down-sampling factor is set as 4. The operations
considered to be time-consuming parts in existing dehazing
algorithms are applied on the down-sampled images, which
lead to a low time complexity. The processing time for images
dehazing with sizes of 1024×768 and 800×600 are about 1.85s
and 0.85s respectively. It is believed to be faster in the GPU-
version. From Fig.1 we can see that the generated transmission
map is smooth except at strong edges. The edge information is
recovered effectively. The proposed approach can remove the
haze effectively while introducing little artifacts.
Cop
y
ri
g
ht © 2012 SciRes.125

Figure 1. From up to down, the original image with fog, the minimun
channel image, the down-sampled minimun channel image, the dark channel,
the patch transmission, the refined transmission, the up-sampled transmission,
the recovered image
Fig.2 shows the comparison with two fast restoration
methods available in the literatures. One of them is developed
by Tarel [9], whose Matlab codes can be found in the website
http://perso.lcpc.fr/tarel.jean-philippe/publis/ iccv09.html. The
other one is to refine transmission map by using guided joint
bilateral filter. It can be seen that the results of presented
algorithm look more natural than the others. The processing
time for images dehazing with sizes of 600×400, as shown in
Fig.2, is about 0.35s using the presented method, compared to
19.50s and 6.35s by using Tarel’s method and bilateral filter
technique respectively. The running speed of the present
method is significantly improved. Note that the test results are
obtained by homemade program using MATLAB language.
Figure 2. From up to down, the original image with fog, the present dehaze
result, the Tarel’s dehaze result, dehaze result using bilateral filter
5. Conclusions
In this paper, a fast and effective method for real-time
image and video dehazing is proposed. Using a newly
presented image prior - dark channel prior, haze removal for a
single image without using any extra information is formulated
as a particular filtering problem and an improved filtering
scheme is proposed based on guided filter. In the presented
algorithm, the airlight and the down-sampled transmission can
be estimated and extracted easily. Then using a guided filter,
the transmission can be further refined and up-samlped. Results
demonstrate the presented method abilities to remove the haze
layer and achieve real-time performace. It is believed that
many applications, such as outdoor surveillance systems,
intelligent vehicle systems, remote sensing systems, graphics
editors, etc, could benefit from the proposed method.
6. Acknowledgment
This work is supported by the China Postdoctoral Science
Foundation funded project. The financial contributions are
gratefully acknowledged.
REFERENCES
[1] Y. Schechner, S. Narasimhan and S. Nayar, “Instant
dehazing of images using polarization,” In Proc. IEEE
Conf. Computer Vision and Pattern Recognition, vol. 1, pp.
1–325, 2001.
[2] S. Shwartz, E. Namer and Y. Schechner, “Blind haze
separation,” In Proc. IEEE Conf. Computer Vision and
Pattern Recognition, vol. 2, pp. 1984–1991, 2006.
[3] S. K. Nayar and S. G. Narasimhan, “Vision in bad
weather,” In Proc. IEEE Conf. Computer Vision, vol. 2, pp.
820–827, 1999.
[4] S. G. Narasimhan and S.K. Nayar, “Interactive
Deweathering of an Image Using Physical Models,” Proc.
IEEE Workshop Color and Photometric Methods in
Computer Vision, in Conjunction with IEEE Int’l Conf.
Computer Vision, Oct. 2003.
[5] J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O.
Deussen, M. Uyttendaele, and D. Lischinski, “Deep Photo:
Model-Based Photograph Enhancement and Viewing,”
ACM Trans. Graphics, vol. 27, no. 5, pp. 116:1-116:10,
2008.
[6] R. Tan, “Visibility in Bad Weather from a Single Image,”
Proc. IEEE Conf. Computer Vision and Pattern
Recognition, June 2008.
[7] R. Fattal, “Single Image Dehazing,” Proc. ACM
SIGGRAPH ’08, 2008.
126 Cop
y
ri
g
ht © 2012 SciRes.

[8] K. He, J. Sun, and X. Tang, “Single image haze removal
using dark channel prior,” In Proc. IEEE Conf. Computer
Vision and Pattern Recognition, 2009.
[9] J. Tarel and N. Hautiere, “Fast visibility restoration from a
single color or gray level image,” In Proc. IEEE Conf.
Computer Vision, pp. 2201–2208, 2009.
[10] Chunxia Xiao, Jiajia Gan, “Fast image dehazing using
guided joint bilateral filter,” Vis Comput, vol. 28, pp. 713-
721, 2012
[11] S.G. Narasimhan and S.K. Nayar, “Vision and the
Atmosphere,” International Journal of Computer Vision,
vol. 48, pp. 233-254, 2002.
[12] K. He, J. Sun and X. Tang, “Guided image filtering,” In
Computer Vision – ECCV 2010, vol. 6311, pp. 1–14, 2010.
Cop
y
ri
g
ht © 2012 SciRes.127