Received: 17 March 2017 Accepted: 19 March 2017

DOI: 10.1002/cav.1776

S P E C I A L I S S U E P A P E R

Sky detection- and texture smoothing-based high-visibility hazeremoval from images and videos

Chunxiao Liu Yiyun Shen Yaqi Shao Jinwei Zhao Xun Wang

School of Computer Science and

Information Engineering, Zhejiang

Gongshang University, Hangzhou, China

CorrespondenceChunxiao Liu, School of Computer Science

and Information Engineering, Zhejiang

Gongshang University, Hangzhou, China.

Email: cxliu@mail.zjgsu.edu.cn.

Funding informationZhejiang Provincial Natural Science

Foundation of China, Grant/Award Number:

LY14F020004; National Natural Science

Foundation of China, Grant/Award Number:

61003188, 61379075 and U1609215; Talent

Young Foundation of Zhejiang Gongshang

University, Grant/Award Number: QZ13-9;

National Key Technology R&D Program,

Grant/Award Number: 2014BAK14B01;

Zhejiang Provincial Commonweal

Technology Applied Research Projects of

China, Grant/Award Number: 2015C33071;

State Key Lab of Virtual Reality Technology

and Systems at Beihang University,

Grant/Award Number:

BUAA-VR-13KF-2013-3; Zhejiang

Provincial Key Laboratory of Electronic

Commerce and Logistics Information

Technology, Grant/Award Number:

2011E10005; Zhejiang Provincial Research

Center of Intelligent Transportation

Engineering and Technology, Grant/Award

Number: 2015ERCITZJ-KF1

AbstractTo address the gloomy sky and the low contrast caused by the left fog in the

existing image dehazing methods, we propose a robust haze removal algorithm for

images and videos. First, a sky detection-based adaptive atmospheric light estimation

method is designed for brighter and cleaner restoration results for the sky regions.

Second, in order to reconstruct a transmission map in line with the depth variation,

we preprocess the input image with texture smoothing to keep the color consistency

inside the same planar object and devise a texture smoothing-based robust trans-

mission estimation method, with which the contrast and color saturation of fog-free

image are greatly promoted. Finally, the restored results are post-processed with

the joint bilateral filter for the purpose of noise removal. What’s more, a guided

filter-based temporally coherent atmospheric light smoothing strategy and a Gaus-

sian filter-based spatial-temporally coherent transmission smoothing strategy are put

forward for video dehazing, which can ensure the spatial as well as temporal conti-

nuity of the haze-free videos. Experimental results show that the recovered haze-free

images and videos have high contrast and color saturation with cleaner sky regions,

and the haze-free videos are free of jittering and flickering phenomena.

KEYWORDSatmospheric light, haze removal, sky detection, temporal coherence, texture smoothing, transmission

1 INTRODUCTION

Haze or fog forms when gas droplets and solid particles are

suspended and mixed in air. The suspended substances in air

scatter light, reduce light quality, and degrade images shot

outdoors, which severely hinder video surveillance, traffic

regulation, and image recognition tasks. As a result, haze

removal from images and videos has become a popular inter-

disciplinary research area of computer vision and computer

graphics.

Image and video dehazing has three main technical chal-

lenges. First, sky regions are prone to color distortion and

gloomy appearance. We put forward an adaptive atmospheric

light estimation method to incur a bright and clean sky.

Second, non-sky regions usually suffer from halo effect and

insufficient contrast enhancement. A robust transmission esti-

mation method is designed to make transmission map in line

with depth variation and get a high contrast non-sky restora-

tion result. Third, the recovered haze-free videos easily appear

jittering and flickering. We propose a temporally coherent

Comput Anim Virtual Worlds. 2017;28:e1776. wileyonlinelibrary.com/journal/cav Copyright © 2017 John Wiley & Sons, Ltd. 1 of 10https://doi.org/10.1002/cav.1776

2 of 10 LIU ET AL.

atmospheric light smoothing strategy and a spatial-temporally

coherent transmission smoothing strategy to avoid jittering

and flickering in the haze-free videos.

2 RELATED WORK

Haze-free images generally have higher contrast than the hazy

ones; therefore, some contrast enhancement-based image

dehazing methods1,2 are proposed. However, they are easy to

cause uncoordinated hue in the recovered results. The current

mainstream image dehazing methods are usually based on the

hazy image degradation model,3 but the lack of depth infor-

mation remains a great challenge. Therefore, some of them are

built upon more than the single hazy image, such as multiple

images taken at the same scene but under different weather4

or polarized with different angles,5 as well as depths of the

scene.6 They have poor practicality due to stringent require-

ments for the input data. Different types of assumptions and

priors make it possible to effectively remove haze from a sin-

gle image. Tan et al.7 remove haze by maximizing the local

contrast of the hazy images, which is prone to local exces-

sive contrast enhancement. Zhu et al.8 estimate transmission

according to the relationship between brightness and satu-

ration of the hazy images, yet they set a uniform scattering

coefficient, which is apt to local insufficient dehazing effects.

Shiau et al.9 employ an extremum approximate method and an

edge preserving mean filter to estimate atmospheric light and

transmission, respectively, and design an 11-stage pipelined

hardware architecture to reduce computing time, but color

distortion still exists.

Compared with existing single image-based dehazing

methods, Fattal’s method 10 and the He et al. method 3 present

better dehazing effects. The former10 is founded on the basis

of the color line prior; however, the color line selection pro-

cess is relatively harsh, resulting in unavailability of most

local transmissions and reduced reliability of transmission

map. The latter3 builds upon the dark channel prior that

applies to most outdoor hazy images, but there are still two

deficiencies. First, sky regions disagree with the dark channel

prior, and the restored haze-free images often present gloomy

or color distorted sky regions. In this regard, Liu et al.11

introduce an adaptive protection factor to increase the trans-

mission values for the sky regions, but it leads to foggy sky

regions with insufficient dahazing effect. Xing et al.12 take

the mean value of sky regions as the atmospheric light, but

it does not apply to hazy images without sky regions. Sec-

ond, transmission should be refined by soft matting3 or guided

filter13 to reduce halo effect, but they tend to trigger unwanted

transmission fluctuations inside the same planar object, which

is detrimental to the contrast enhancement. In this regard,

Zhao et al.14 describe a patch shift-based transmission esti-

mation strategy to reduce halo effect, but residual halo effect

is roughly processed with replacement, which will generate

visual defects around object boundaries. Lai et al.15 estimate

the transmission map by solving a large constrained matrix

to avoid unreasonable transmission fluctuations. However, it

is inefficient, and color distortion still exists in the restored

results.

Although video dehazing has broad application scenarios as

well, less research work has been done. Gibson et al.16 obtain

the transmission sequence with motion vector estimation, but

it causes flickering if there isn’t motion within local patch.

Chen et al.17 suppress visual artifacts by minimizing residual

gradients, but it is detrimental to contrast enhancement. Com-

pared with the existed video dehazing methods, Kim et al.18

present a better one. It optimizes the contrast enhancement

term and the information loss term simultaneously and cal-

culates transmission for each frame with overlapped image

patches, but it has serious halo effect and flickering phenom-

ena around the boundaries of the moving objects.

To address the aforementioned deficiencies among the

existing haze removal methods, we propose a sky detection-

and texture smoothing-based robust haze removal algorithm

for images and videos. It aims for brighter and cleaner sky

regions without color distortion, and higher contrast and satu-

ration for the non-sky regions. It results in coherent haze-free

videos without jittering and flickering.

3 OUR ROBUST IMAGE AND VIDEODEHAZING ALGORITHM

The haze imaging model3 can be expressed as

I(x) = J(x)t(x) + A(1 − t(x)), (1)

where I(x) represents the color value of pixel x in the input

hazy image I, J is the expected haze-free image, t is the trans-

mission, and A is the atmospheric light. The transmission tcan be expressed by the following formula3:

t(x) = e−𝛽d(x), (2)

where d(x) represents the depth value of pixel x and 𝛽 is the

scattering coefficient.

The sky regions do not conform to the dark channel prior3

and will appear with color distortion or gray phenomena

in the restored results. Considering that transmission adjust-

ment fails for the sky regions, atmospheric light A is used

to optimize sky regions, and a sky detection-based adaptive

atmospheric light estimation method is proposed here. For

the hazy images with sky, bright sky restoration results with

significant dehazing effects can be achieved. Then, texture

smoothing is applied to preprocess the input image, and a tex-

ture smoothing-based robust transmission estimation method

is proposed to avoid the transmission fluctuations inside the

planar object with same depth. Thus, for those regions that

LIU ET AL. 3 of 10

FIGURE 1 Our image dehazing flowchart

conform to the dark channel prior, the contrast will be pro-

moted in the restored results. At last, joint bilateral filtering

is used to eliminate the noise influence in the recovered

haze-free image. Figure 1 shows the flowchart of our image

dehazing algorithm.

For video dehazing, a temporally coherent atmospheric

light smoothing strategy and a spatial-temporally coherent

transmission smoothing strategy are designed to guarantee the

spatial and temporal coherence of the haze-free videos and

avoid jittering and flickering phenomena.

3.1 Sky detection-based adaptiveatmospheric light estimationEquation 1 shows that when t(x) approaches zero, I(x) will

approach A; therefore, A can be approximated by the inten-

sity of most haze-opaque regions in the hazy image. For the

images with sky, the estimated atmospheric light values from

the previous methods3 will be higher than most of the sky

pixel values, which results in darker sky pixels and gloomy

sky regions in the restored results. Conversely, if the atmo-

spheric light values are lower than most of the sky pixel

values, the recovered sky regions will be brighter. Thus, we

design an adaptive atmospheric light estimation method to get

relatively small atmospheric light values for the images with

sky, which can make the restored sky regions brighter and

cleaner.

A support vector machine (SVM)-based atmospheric light

validation method14 is carried out to obtain the initial

atmospheric light A′. And a one-dimensional histogram

segmentation-based sky recognition method11 is adopted to

detect the sky regions Ωsky. If Ωsky ≠ 𝜙, sky pixels are sorted

by luminance. In order to get smaller atmospheric light values

and avoid the noise influence from the sky detection result, the

sky pixels with the minimum 0.1% to 0.5% luminance values

are chosen and denoted as Ω′sky. Thus, the atmospheric light

values can be taken as

A ={ meanx∈Ω′

sky(I(x)), Ω′

sky ≠ 𝜙;A′, Ω′

sky = 𝜙,(3)

where mean(·) is an average operator and I is the input hazy

image. If the images contain sky regions, the obtained atmo-

spheric light values are lower enough to ensure a brighter

sky restoration result, which is also reasonable because the

depth of the sky is infinite. If the images do not hold sky

regions, the atmospheric light values are taken as A′ directly.

Because SVM-based atmospheric light validation method14

can effectively avoid the interference of bright objects such

as car lights, the location of the atmospheric light can prop-

erly reflect the meaning of most haze-opaque regions on the

image.

Figure 2 shows an image dehazing example with sky. The

red and blue regions in Figure 2(c) are the locations of atmo-

spheric light estimated by the He et al. method3 and our

method, respectively, which show that our atmospheric light

values are smaller. Figure 2(h) shows the haze-free image

gained by replacing our adaptive atmospheric light estima-

tion module with that of the He et al. method.3 Compared

with the input image, the sky region in Figure 2(h) is gloomy

with serious color distortion. On the contrary, our sky restora-

tion result is brighter and cleaner. For the hazy image without

sky in Figure 3, the SVM-based atmospheric light validation

method14 is directly used to estimate the atmospheric light

values. The red boxes in Figure 3(b) indicate the rejected

atmospheric light positions, and the green box represents the

finally accepted atmospheric light position.

3.2 Texture smoothing-based robusttransmission estimationThe He et al. method3 generates halo effect near object bound-

aries. The existing solutions for halo effect elimination3,13 are

to refine the coarse transmission map under the guidance of

the input image, which achieves consistent edge information

between the transmission map and the input image. However,

they will result in unwanted texture fluctuations inside the

planar objects and reduce the dehazing effect in the halo adja-

cent regions, which is detrimental to the contrast enhancement

of haze-free images. Thus, a texture smoothing-based robust

transmission estimation method is proposed to solve the above

problems.

Texture smoothing is first applied to preprocess the input

image with L0 gradient minimization filter,19 that is,

arg minI′ {∑

p(I′p − Ip)2 + 𝜆C(I′)},C(I′) = #{p|||𝜕xI′p|| + ||𝜕yI′p|| ≠ 0}, (4)

where I′p is the color value of pixel p after texture smooth-

ing, I is the input image, C(I′) is the number of pixels with

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FIGURE 2 Haze removal example for the “Tiananmen” image with sky. (a) input image, (b) sky detection, (c) atmospheric light estimation, (d)

preliminary transmission t, (e) fine transmission map with (a), (f) texture smoothing of (a), (g) fine transmission map with (f), (h) haze removal with

old A,3 (i) our haze removal result

greater gradient than 0 in I′, and 𝜆 is the smoothing coef-

ficient. L0 gradient minimization filter cannot only suppress

texture, making pixel values inside the same planar object as

consistent as possible, but also enhance structure, avoiding

different planar objects being smoothed together. According

to lots of experiments on the hazy images, the best filtering

parameters are taken as 𝜆 ∈ (0.01, 0.05). Considering that

𝜆 values in the narrow span will not lead to big differences

among the smoothing results, we set a default value for 𝜆 as

0.03 for the convenience of the users.

Then, we obtain a preliminary estimated transmission map

t from I′ and A with a patch shift-based transmission cal-

culation strategy,14 which can effectively reduce unreliable

transmission. And only few complex structure pixels remain

with halo effect in the t recovered haze-free images, which

are resolved with the guided filter13 to refine t as t under the

guidance of I′. Because the texture details in I′ are effectively

suppressed and the pixel values inside the same planar objects

are similar after the texture smoothing process,19 the refine-

ment operation under the guidance of I′ cannot only maintain

the transmission consistency inside the planar objects but also

keep transmission variation with the depth variation where

usually the structure in the input image are.

In Figure 3(d) and 3(g1), the refined transmission map

under the guidance of I takes on obvious texture fluctuations

on the wall with approximately same depth, which is incon-

sistent with the depth variation. Figure 3(e) shows the texture

smoothing result I′ of I′, where textures are suppressed and

pixel values inside are consistent on the wall. Figure 3(f)

and 3(g2) display the refined transmission map under the

guidance of I′, where transmissions inside the same planar

objects maintain consistency. Comparing Figure 3(h) and 3(i),

we can find that our result has higher contrast and saturation

than the He et al. result.3

3.3 Joint bilateral filtering-basedpost-processingLet’s substitute A obtained by the adaptive atmospheric light

estimation method and t obtained by the robust transmission

estimation method into Equation 1 for single image dehaz-

ing and denote the haze-free image as J. J is full of color,

LIU ET AL. 5 of 10

FIGURE 3 Haze removal example for the “Mansion” image without sky. (a) input image, (b) atmospheric light validation, (c) preliminary

transmission t, (d) refined transmission with (a), (e) texture smoothing of (a), (f) refined transmission with (e), (g1) local magnification of (d), (g2)

local magnification of (f), (h) our defogging result, (i) the He et al. result3

with high contrast and visibility; however, noise appears in

the original dark area. Here, the joint bilateral filter is used to

post-process J as follows:

Jp = 1

Kp

∑q∈Ωp

Jqf (p − q)g(Ip − Iq), (5)

where Jp represents the color value of pixel p in the

image after post-processing, I is the input image, f (x) =exp(−||x||2∕𝜎2

s ) is the Gaussian weight function in the spatial

space, g(x) = exp(−||x||2∕𝜎2r ) is the Gaussian weight function

in the color space, and Kp is the sum of weights of all pixels

in a local region Ωp centered on p. Joint bilateral filter refers

to both spatial information of pixels and color information of

I, which can maintain image edge while removing noise.

Figure 4(b) and 4(c) display the haze-free images J and

J before and after post-processing, respectively. From their

local magnifications, Figure 4(d1) and 4(d2), we can see that

noise in the haze-free image is effectively suppressed, while

the detail and edge information are well preserved.

3.4 Spatial-temporally coherent videodehazingRecovering each frame of the hazy videos with single image

dehazing algorithm directly will result in hue jittering and

local flickering phenomena in the haze-free videos due

to the temporal incoherence of atmospheric lights as well

as transmissions among adjacent frames. Consequently, the

spatial-temporal smoothing of the atmospheric lights and the

frame-wise texture smoothed videos will be helpful.

To address the mutations of atmospheric lights, a guided

filter-based temporally coherent atmospheric light smooth-

ing strategy is proposed. Let’s denote the average luminance

sequence of the video as SL(n) and the atmospheric light

sequence of the video as S′A(n), where n is the number of

the frame. Because SL(n) are usually smooth among adja-

cent frames, we smooth S′A(n) under the guidance of SL(n) by

one-dimensional guided filter and get SA(n), which can avoid

abrupt hue and luminance variations in the haze-free videos.

Nevertheless, in order to prevent the atmospheric lights from

the influence of sudden SL(n) changes, we further slow down

the changes of the atmospheric lights as

A(n) = 𝛼 ∗ A(n − 1) + (1 − 𝛼) ∗ SA(n), (6)

where A(n) is the atmospheric light of frame n ⩾ 2 and the

damping coefficient 𝛼 is taken as 0.95 here.

To address the temporal and spatial incoherence of the

transmissions, a Gaussian filter-based spatial-temporally

coherent transmission smoothing strategy is proposed to fur-

ther smooth the frame-wise texture smoothed videos.

6 of 10 LIU ET AL.

FIGURE 4 Joint bilateral filtering-based post-processing result

FIGURE 5 Comparison of different image defogging methods. (a) input images, (b) the He et al. results,3 (c) the Fattal’s results,10 and (d) our

results

LIU ET AL. 7 of 10

I′p(n) =∑Δn

∑q∈Ωp

f (p − q,Δn)I′q(n + Δn)K

, (7)

where I′p(n) is the expected color value for pixel p in frame

n and I′ is the frame-wise texture smoothed result of original

frame I. Ωp is a 3 × 3 local image patch centered on pixel p,

Δn ∈ [−7, 7] indicates the front and rear 7 frames centered

on the current frame, f (x, y) = exp(−(||x||2 + ||y||2)∕𝜎2) is

a Gaussian weight function, and K is the normalized weight

coefficient. I′(n) is used with A(n) to get the transmissions and

the restored result of frame n.

Our video dehazing examples are shown in Figure 7, which

achieve our goal in restoring high-visibility haze-free results

while avoiding jittering and flickering phenomena.

4 EXPERIMENTAL RESULTS ANDDISCUSSIONS

Figures 5 and 6 show the comparison of image dehazing

effects between our algorithm and five existing methods.

Figure 7 shows the comparison of video dehazing effects

between our algorithm and the Kim et al. method.18

Figure 5 compares the dehazing effects among the He et al.

results,3 Fattal’s results,10 and our results. Example (1) shows

that the dehazing intensity of both the He et al. result3 and

Fattal’s result10 are insufficient. Our result is brighter, and

the contrast and saturation among wall and leaves are higher.

Example (2) reveals inconsistent dehazing degrees around

middle branches in the He et al. result,3 resulting in visual

disharmony, and color distortion around left branches in

Fattal’s result.10 Our result is bright with vivid color, without

visual disharmony or color distoration. Example (3) indicates

that our result has higher contrast and saturation than the

He et al. result3 with bad overall dehazing effect, as well as

Fattal’s result10 with low overall color fluctuations.

Figure 6 compares our algorithm with three dark channel

prior-based improved image dehazing methods. The Liu et al.

results11 have color distortion phenomena in Examples (1)

and (3) and foggy sky without sufficient dehazing intensity in

Examples (2) and (4). The Xing et al. method12 will lead to

serious over-exposure for images without sky as Example (1)

and present low contrast and saturation in all examples as well

FIGURE 6 Comparison with three dark channel prior-based improved image dehazing methods. (a) input images, (b) the Liu et al. results,11 (c)

the Xing et al. results,12 (d) the Zhao et al. results,14 and (e) our results

8 of 10 LIU ET AL.

FIGURE 7 Video dehazing comparison with the Kim et al. method18

as color distortion phenomena in Example (3). The Zhao et al.

results 14 have low contrast in Example (1) and serious color

distortion phenomena in sky regions in the other examples.

On the contrary, our results hold high contrast and saturation

without color distortion phenomena as well as clear and bright

sky regions.

Figure 7 compares video dehazing effects from our

algorithm and the Kim et al. method.18 Example (1) shows

that our results have higher contrast and keep the same warm

colors as the hazy video; however, the Kim et al. results18 look

a little cold. Examples (2) and (3) show that the Kim et al.

results18 have halo effect around object boundaries, resulting

in serious flickering phenomena. On the contrary, our results

have higher contrast and brighter sky regions, without halo

effect. For relevant videos, please refer to our multimedia

attachments.

5 CONCLUSIONS

To address the gloomy sky issue of existing image dehaz-

ing methods, a sky detection-based adaptive atmospheric

light estimation method is proposed to avoid color distor-

tion and recover a bright and clean sky. To address the

insufficient contrast enhancement issue caused by incon-

sistency between transmission map and depth information,

a texture smoothing-based robust transmission estimation

method is proposed for maintaining transmission consistency

LIU ET AL. 9 of 10

inside the planar object. It effectively enhances the contrast

and saturation of non-sky regions in the restored images

and videos. Finally, a guided filter-based temporally coher-

ent atmospheric light smoothing strategy and a Gaussian

filter-based spatial-temporally coherent transmission smooth-

ing strategy are proposed for maintaining the temporal and

spatial coherence of haze-free videos that don’t have jittering

and flickering phenomena.

However, over enhancement of color saturation can cause

color distortion, such as the window glass shown in

Figure 5(a). And the accuracy of sky detection will sometimes

have impact on atmospheric light estimation. These two

defects are what we are trying to solve in our following

research work.

ACKNOWLEDGEMENTSThis work is supported by the Zhejiang Provincial Natural

Science Foundation of China under grant no. LY14F020004,

the National Natural Science Foundation of China under

grant nos. 61003188, 61379075, and U1609215, the Tal-

ent Young Foundation of Zhejiang Gongshang University

under grant no. QZ13-9, the National Key Technology

R&D Program under grant no. 2014BAK14B01, the Zhe-

jiang Provincial Commonweal Technology Applied Research

Projects of China under grant no. 2015C33071, the open

funding project of State Key Lab of Virtual Reality Tech-

nology and Systems at Beihang University under grant

no. BUAA-VR-13KF-2013-3, the Zhejiang Provincial Key

Laboratory of Electronic Commerce and Logistics Infor-

mation Technology under grant no. 2011E10005, and the

Zhejiang Provincial Research Center of Intelligent Trans-

portation Engineering and Technology under grant no.

2015ERCITZJ-KF1.

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Chunxiao Liu is an associate

professor at the School of Com-

puter Science & Information

Engineering, Zhejiang Gong-

shang University, China. He

received his PhD degree in

Mathematics from the State Key

Lab of CAD&CG, Zhejiang

University in 2009. His research interests include

image and video-based rendering, smart video surveil-

lance, pattern analysis and intelligent systems, and

computer vision.

Yiyun Shen is now a junior

majored in Computer Sci-

ence and Technology at the

School of Computer Science

and Information Engineering,

Zhejiang Gongshang Univer-

sity, China. His current research

10 of 10 LIU ET AL.

interest is visual computing and

understanding.

Yaqi Shao is currently work-

ing towards the BS degree in

Computer Science and Technol-

ogy at the School of Computer

Science and Information Engi-

neering, Zhejiang Gongshang

University, China. Her research

interests focus on machine learn-

ing for computer vision.

Jinwei Zhao is currently

working towards the BS

degree in Computer Sci-

ence and Technology at the

School of Computer Science

& Information Engineering,

Zhejiang Gongshang Uni-

versity, China. His research

interests include computer vision and computer

graphics.

Xun Wang is a professor and

dean at the School of Computer

Science and Information Engi-

neering, Zhejiang Gongshang

University, China. He received

his BSc in Mechanics, MSc, and

PhD degrees in Computer Sci-

ence, all from Zhejiang Univer-

sity. His current research interests include visual media

computing, intelligent information processing, multi-

media information security, and geographic informa-

tion system.

How to cite this article: Liu C, Shen Y, Shao Y,

Zhao J, Wang X. Sky detection and texture smooth-

ing based high visibility haze removal from images and

videos. Comput Anim Virtual Worlds. 2017;28:e1776.

https://doi.org/10.1002/cav.1776

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