Vis Comput (2011) 27: 749–757DOI 10.1007/s00371-011-0569-8

O R I G I NA L A RT I C L E

Video dehazing with spatial and temporal coherence

Jiawan Zhang · Liang Li · Yi Zhang · Guoqiang Yang ·Xiaochun Cao · Jizhou Sun

Published online: 20 April 2011© Springer-Verlag 2011

Abstract This paper describes a new framework for videodehazing, the process of restoring the visibility of the videostaken under foggy scenes. The framework builds upon tech-niques in single image dehazing, optical flow estimation andMarkov random field. It aims at improving the temporal andspatial coherence of the dehazed video. In this framework,we first extract the transmission map frame-by-frame usingguided filter, then estimate the forward and backward opticalflow between two neighboring frames to find the matchedpixels. The flow fields are used to help us building an MRFmodel on the transmission map to improve the spatial andtemporal coherence of the transmission. The proposed algo-rithm is verified in both real and synthetic videos. The re-sults demonstrate that our algorithm can preserve the spatialand temporal coherence well. With more coherent transmis-sion map, we get better refocusing effect. We also apply ourframework on improving the video coherence on the appli-cation of video denoising.

Keywords Video dehazing · Markov random field · Imagedehazing · Video enhancement

J. Zhang · L. Li (�) · G. Yang · X. Cao · J. SunSchool of Computer Science and Technology, Tianjin University,Tianjin, Chinae-mail: allen.liliang@gmail.com

J. Zhang · L. Li · Y. Zhang · G. Yang · X. Cao · J. SunMTS Lab, Tianjin University, Tianjin, China

J. Zhang · Y. ZhangSchool of Software, Tianjin University, Tianjin, China

1 Introduction

The photographs and videos we get in our daily life are eas-ily plagued by the aerosols suspended in the medium. Therays reflected by the objects’ surfaces are not only attenu-ated by the aerosols but also blended with the airlight [14],which makes the image/video contrast lost and/or vividnesslost.

As the degradation is spatial-variant, it is a challenge torecover the color and details of scene from the foggy im-ages/videos. General contrast enhancement techniques, suchas histogram equalization and gamma correction, may notgenerate satisfying results. Therefore, many methods, suchas using multiple images [17, 19, 20] or additional informa-tion [13], have been proposed.

In the context computational photography, single imagehaze removal algorithms have made significant progress.Various methods [2, 8, 9, 11, 16, 22, 27] have been proposedbased on a variety of priors or assumptions. The advantageof single image dehazing is that it needs minimal input andmay be valid for most scenes. In Sect. 2 we will give a briefoverview of these methods.

Compared to the extensive work on image dehazing, littlework has been done on video dehazing. Extending image-dehazing algorithm to video is not a trivial work. The chal-lenges mainly come from the following aspects:

– Computational efficiency. The algorithm must be able toefficiently process the large number of pixels in a videosequence. In particular, the user prefers less interactionand minimal input of data.

– Temporal coherence. It has been proven that human vi-sualization system (HVS) is very sensitive to tempo-ral inconsistencies presented in video sequences [24].However, applying image-dehazing algorithm naively on

750 J. Zhang et al.

Fig. 1 Spatial inconsistency is (a) the foggy image, (b) the dehazedresult using the method proposed in [23]. Note the rectangular region.Tarel’s method [23] cannot well handle the region with discontinuousdepth

Fig. 2 The foggy video and the results we get. (a) One frame in theoriginal foggy video. (b) The dehazed frame

frame-by-frame often results in visual flicker. Figure 7 isan example of temporal coherence.

– Spatial consistency. The recovered video should be as nat-ural as the original one. It is important to handle the inner-frame discontinuities in the video. Figure 1 shows the dis-continuities in single-image dehazing.

In this paper, we present a method for restoring visibil-ity from foggy videos, which can remove haze from videosand preserve the temporal and spatial coherence. We em-ploy an efficient single-image-dehazing algorithm to per-form per-frame processing to the foggy video, which out-puts a sequence of transmission map of the original video.To improve the temporal coherence of the transmission mapsequence, we leverage the optical flow techniques to find thecorresponding pixels between neighboring frames. We gen-erate forward and backward accuracy map to determine theaccuracy of the flow field. Finally, based on the accuracymap, we utilize the Gauss–Markov random field to improvethe spatial and temporal coherence of the transmission map.By the refined transmission map, we get a more visual co-herent dehazed video. Figure 2 shows the result of our de-hazing algorithm.

This paper is organized as follows. We begin by introduc-ing the techniques our work built upon in Sect. 2. Then, we

describe our video dehazing algorithm in Sect. 3. In Sect. 4we will show the results and compare our algorithm withthat of the others’. We will also show other applications ofour algorithm in Sect. 4. Finally, the limitations of our algo-rithm and the future work are given in Sect. 5.

2 Related work

In this section, we will introduce the work related to our al-gorithm, including single-image dehazing, optical flow esti-mation and temporal-spatial coherence improving.

Recently, single-image dehazing has made significantprogress [2, 8, 9, 11, 16, 22, 27]. The success of these meth-ods lies in a strong prior or assumption.

Many algorithms [11, 27] regard the transmission as alow frequency variable, i.e., assuming it varies smoothly inspatial domain. They either use Gaussian low-pass filter orassume the transmission is locally constant to get the crudetransmission map and then refine it to get the final one.

Tan’s work [22] is based on two observations. One is thatclear-day images have more contrast than images plaguedby bad weather; the other is that the variations of airlight,which mainly depends on the distance of objects to theviewer, tend to be smooth. Fattal [9] assumes that the trans-mission and the surface shading are locally uncorrelated. Heet al. [11] propose Dark Channel Prior to solve the single-image-dehazing problem. Dong et al. [8] incorporate thesparse prior of nature image into the dehazing framework.

Optical flow is widely used in video processing [7]. Itgives an estimation of the motion between the frames, whichis quite useful for temporal coherence.

There are numerous methods to calculate optical flow.Baker et al. [3] give a modern survey of optical flow estima-tion. Efforts have been put into improving the optical flowconstraints. Since the quadratic formulation is not robustto outliers, Black and Anandan [5] replaced the quadraticerror function with a robust formulation. Haussecker andFleet [10] proposed a physical constraint to model bright-ness change.

More recently, Xu et al. [25] provided a detailed preserv-ing method, which can work well even when there are verysmall flow structures in the flow field. Although it achievesmore accurate results, the complexity of the algorithm isvery high, which makes it not very suitable for video pro-cessing.

In [21], Sun et al. uncover what has made the recent ad-vances in the estimating optical flow by a thorough analysisof how the objective function, the optimization method in-fluence accuracy. Besides, Sun et al. derive a new objectivethat formalizes the median filtering heuristics, which is a keyto recent performance gains.

Markov Random Field (MRF) is quite useful for model-ing spatial context and stochastic interaction among observ-

Video dehazing with spatial and temporal coherence 751

Fig. 3 Video dehazing flow chart. We first use guided filter to get therough transmission map, then use the method proposed in [21] to es-timate the corresponding pixels between neighboring frames. With theflow field, we get the forward and backward error maps. Based on the

error maps, we build the MRF to proceed with a statical smoothingon the rough transmission map along temporal dimension. Finally, werestore the visibility using the refined transmission map to get the de-hazed video

able quantities in many practical problems. As a result, MRFhas generated a substantial amount of excitement in imageprocessing, computer vision, and applied statistics.

In the context of video denoising, Chen and Tang [6]present a spatiotemporal MRF for video denoising. TheMRF they constructed is based on a piecewise smoothnessprior. The main advantage of the proposed MRF is that itintegrates spatial and temporal information adaptively into astatistical inference framework. For image matting, Lin andShi [15] proposed an MRF-based approach for image mat-ting with complex scene. Fattal [9] used MRF to apply astatistical smoothing to the intermediate results.

3 Video dehazing

We incorporate the previously introduced techniques in ourvideo dehazing framework. The flow chart of our frameworkis shown in Fig. 3.

The imaging model widely used for the foggy video isgiven by (1)1

I(x, s) = t (x, s)J(x, s) + (1 − t (x, s)

)A, (1)

where x denotes the location of one pixel in frame s, I isthe foggy video, J is the scene radiance in clear days, t isthe medium transmission describing the portion of the lightthat is not scattered and reaches the camera, and A is theglobal atmospheric color. The goal of image restoration isto recover t (x, s), J(x, s) and A for each pixel x in eachframe s.

1The temporal dimension is denoted by s to differentiate between thetransmission maps.

We assume the atmospheric color A is constant in thewhole video, which makes it easy to estimate. Most image-dehazing algorithms estimate A from the pixels with highestintensities, which is fast but not accurate. For example, thebrightest pixel maybe a white building or a white car. He etal. [11] integrate the airlight estimation with the dark chan-nel prior and it makes the estimation result more accurate.Here we adapt the method they proposed.

Given the atmospheric color A, we proceed with the fol-lowing steps to restore the visibility of the foggy image.

3.1 Frame-by-frame dehazing

For each frame s, we first get the crude transmission mapt̃ (x, s) using dark channel prior

t̃ (x, s) = 1 − ω minc∈r,g,b

(min

y∈Ω(x)

(Ic(y)

Ac

)), (2)

where ω is a parameter for keeping some amount of hazefor distant object, which is useful for some applications. Assuggested in [11], the ω is fixed to be 0.95.

Then, we incorporate the guided filter [12] algorithm torefine the crude transmission map t̃ (x, s). For each frame s,it assumes the refined transmission map t̊ (x, s) is a lineartransform of the foggy (guidance) image I(x, s) in a win-dow ωx centered at pixel x:

t̊ (y, s) = aTx I(y, s) + bx, ∀y ∈ ωx, (3)

where ax and bx are linear coefficients assumed to be con-stant in ωx . To differentiate between the output t̊ (x, s) andthe input t̃ (x, s) as little as possible, we minimize the follow-

752 J. Zhang et al.

Fig. 4 Inner-frame processing. (a) 3rd frame of the original video I.(b) The crude transmission t̃ (x,3) taken from (2). (c) The refined trans-mission t̊ (x,3) using guided filter

ing cost function in the local window ωx centered at pixel x:

E(ax, bx) =∑

y∈ωx

((aTx I(y, s) + bx − t̃ (x, s)

)2 + εa2k

). (4)

The small variable ε is a regulation parameter preventingax from being too large. The solution to (4) is given by

ax = (Σx + εU)−1(

1

|ω|∑

y∈ωx

I(x, s)t̃ (x, s) − μx t̄(x, s)

),

bx = t̄ (x, s) − aTxμk,

(5)

where Σx is the 3×3 covariance matrix of I(x, s) in ωx , U isa 3 × 3 identity matrix, t̄ (x, s) is the mean value of the inputt̃ (x, s) in window ωx and μk is the mean vector of I(x, s) inwindow ωx .

By substituting (5) into (3), we get the refined trans-mission map t̊ (x, s) for each frame. The result is shown inFig. 4.

3.2 Optical flow estimation

Optical flow algorithm can be used to estimate the inter-frame motion at each pixel in a video sequence. For twoneighboring frames, I(x, s) and I(x, s + 1), the most basicassumption made in optical flow calculation is image bright-ness constancy. In foggy image pair, the brightness of cor-responding pixels is not exactly equal to each other due tothe change of depth. Fortunately, the change is very tiny be-tween two adjacent frames in a video. We regard that thebrightness constraint is also valid:

I(x, s) ≈ I(x + u, s + 1), (6)

where u describes the velocity of the pixel. To reduce thebrightness difference in two frames, we perform pre-filteringas suggested in [5]. In our video dehazing process, we in-corporated Sun’s most recent optical flow estimation algo-rithm [21], for which an implementation is available on theauthor’s web page.

In order to use optical flow to its fullest advantage, wefollow the method proposed in [7]. For each frame s, we

Fig. 5 Forward and backward error maps. First row: frame 3, 4, 5 ofthe original video. Middle row: forward flow field, backward flow fieldand the color code to draw the field. Last row: forward error map M

f

4and backward error map Mb

4

calculate two kinds of flow field, i.e. forward flow ufs from

frame s − 1 to frame s and backward flow ubs from frame

s + 1 to frame s. To determine the accuracy of these twoflow fields, we create the forward error map M

fs and the

backward error map Mbs . For a pixel x in frame s, the corre-

sponding pixel in frame s − 1 is x − ufs and in frame s + 1

is x − ubs . We compute the difference between the predicted

color and the observed color in RGB color space,

Mfs = ∥∥I (x, s) − I

(x − uf

s , s − 1)∥∥

2,

Mbs = ∥∥I (x, s) − I

(x − ub

s , s + 1)∥∥

2,(7)

where ‖ · ‖2 is the L2-norm of a vector. The forward andbackward error maps give us a measure at each pixel of theaccuracy of flow estimation from the previous and followingframes. The error maps are shown in Fig. 5. We use thisinformation to construct the MRF to improve the temporalcoherence.

3.3 Temporal coherence improvement

With the assumption that the transmission of the corre-sponding pixels varies smoothly, we proceed with a staticalsmoothing along the temporal dimension using MRF to im-prove the spatial and temporal coherence of t̊ (x, s), which isinspired by [9].

Video dehazing with spatial and temporal coherence 753

Fig. 6 The MRF model definedto improve the spatial andtemporal coherence of thetransmission map sequence

For a foggy video with n frames, the 3D MRF model isdefined by (8):

P(t) ∝n∏

s=1

∏

x∈I(x,s)

exp(−(

t (x, s) − t̊ (x, s))2

/σ 2p

)

×∏

∀y∈Ωx

exp(−Ps(x,y, s) · (t (x, s) − t (y, s)

)2/σ 2

s

)

×∏

∀c∈{f,b}exp

(−Pt(x, c, s)

× (t (x, s) − t

(x − uc

s , s′))2

/σ 2t

), (8)

where Ωx is the set of pixel x’s four nearest neighbors inspatial domain, uc

s is either ufs or ub

s . When c = f , thens′ = s − 1 and when c = b, then s′ = s + 1. Ps(x,y, s) andPt(x,y, s) are the spatial and temporal priors, respectively.The spatial prior is defined as

Ps(x,y, s) = 1

(t̊(x, s) − t̊ (y, s))2, (9)

and the temporal prior is defined as

Pt(x, c, s) = 1

Mcs (x)

, ∀c ∈ {f,b}. (10)

For each pixel x in frame s, x − ucs is the corresponding

pixel in frame s′. The first term in (8) defines the expectationof the final transmission map. The second term describesthe spatial prior in each frame and the last one describes thetemporal priors of adjacent frames. The link defined by (8)is illustrated in Fig. 6.

We maximize the probability by solving the linear systemresulting from d logP/dt = 0 and take this optimum to beour final transmission map.

Given the final transmission t (x, s) for each frame, assuggested in [11], we recover the visibility using (11):

J(x, s) = I(x, s) − A

max(t (x, s), t0)+ A, (11)

where t0 is some lower bound to prevent the result fromnoise. Here we set t0 = 0.1 in our experiments.

4 Results and comparison

In our experiments, we implement the guided filter usingC++. For the linear system generated by MRF, we use Pre-conditioned Conjugate Gradient (PCG) algorithms as oursolver. It takes about 30–40 seconds to get the transmissionmap for each frame using guided filter and 40–50 secondsto solve the large sparse linear equation for a 600×400 res-olution 40-frame video on a PC with a 2.3 GHz PentiumDual-Core processor.

We have conducted experiments on both synthetic andreal foggy videos to test the proposed algorithm. First wecompare our method with the frame-by-frame video dehaz-ing algorithms. Figure 7 shows our result. Note for the leavesin the area marked with rectangular. The naive algorithmcannot preserve the temporal coherence while our algorithmpreserves it well.

The evaluation of visibility restoration is difficult on realvideos since no reference is available. So we synthesize afoggy video with “flat” fog, i.e. we assume the depth is aconstant over the video, and then we restore the visibilityof the synthetic video. We compare the mean absolute dif-ference between consecutive frames of the dehazed videoto show our algorithm can preserve the temporal coherencewell. Figure 8 presents the results.

Applications The MRF model we proposed here can beused not only for restoring visibility but also for video de-

754 J. Zhang et al.

Fig. 7 Our results. (a) Two frames in a foggy video. (b) The results using the naive frame-by-frame method. (c) The results using our algorithm.(d) and (e) is the zoom of the rectangular region in (b) and (c). Note the leaves in the rectangular area. Frame-by-frame method cannot preservethe intensities of the leaves in neighboring frames unchanged

Fig. 8 Synthetic experiments. (a) The original sequence. (b) The re-sult of adding the “flat” fog with constant t = 0.6. (c) The result of ourdehazing algorithm. (d) The mean absolute difference of the originalvideo, the naively frame-by-frame algorithm and our algorithm

noising, video matting or other video processing. We justneed to modify the spatial and temporal priors. Figure 9 is anexample for video denoising. The videos we use are down-loaded from [1]. We first denoise the video frame-by-frameusing the Gaussian scale mixture (GSM) method, then buildthe MRF model on the denoised video to improve the tem-poral coherence. Figure 9(d) shows the result of comparing

Fig. 9 Application on video denoising. (a) Original frame taken fromSuzie sequence [1]. (b) Noisy frames. The noise we added is theGaussian white noise, with mean value μ = 0 and standard varianceσ = 0.03. (c) The denoised result using our algorithm. (d) The com-parison of mean absolute difference

of our algorithm with GSM. One can see from the meanabsolute difference comparison that the sequence processedby our algorithm has much better coherence than the GSMmethod.

The predicted transmission map can be used for otherapplications on top of haze removal, such as changing thefocus point of the video. Since the transmission map gains

Video dehazing with spatial and temporal coherence 755

Fig. 10 The refocused effect. (a) The original frames. (b) Dehazed results using our algorithm. (c) Depth maps we get. (d) Refocused effects. Ineach frame, the focus plane is set on the tree

Fig. 11 The failure case. (a) The original image. (b) Transmissionmap recovered using dark channel prior. Since most of the scene iscovered with snow, whose color is inherently similar to the atmospherelight, the transmission map is underestimated

more temporal coherence, the effect of refocus is moresmooth. Figure 10 shows our result of changing the focusplane.

5 Conclusion and future work

In this paper, we have proposed an algorithm to remove hazefrom a video while preserving the temporal and spatial co-herence. We first apply guided filter to get transmission mapfor each frame. Then we proceed with a statical smoothingusing MRF to smooth the transmission in temporal and spa-tial dimension.

Our algorithm only improves the coherence of the trans-mission map, while not making the result more exact. So itsuffers from the same limitation as with dark channel prior.When the scene objects are inherently similar to the atmo-spheric light and no shadow is cast on them, the recoveredtransmission map is not accurate. The failure case is shown

Fig. 12 The scenes to the effect that the simple imaging model is notvalid. A more complicated model [18] may be used to model thesescenes

in Fig. 11. Since the color of snow is similar to that of fog,the transmission of the snow is underestimated.

For future work, we intend to explore more constraintsbetween frames, such as the geometry constraints proposedin [26]. Although it may be more computationally complex,the recovered result is more accurate.

The model we used may be invalid for complicated scene,such as the sun’s influence on the sky region which isshown in Fig. 12. To deal with these scenes, more advancedmodel [18] can be used. We intend to investigate the hazeremoval based on these models in the future.

Besides, it is more computationally effective to solve thelarge sparse linear system in GPU. We plan to implementthe sparse linear system solver on GPU [4] for the futurework.

Acknowledgements This work is partially supported by the Na-tional Natural Science Foundation of China (Grant No. 60905019)and Tianjin Science and Technology Support Program (No. 10ZCK-FGX00800).

756 J. Zhang et al.

References

1. http://media.xiph.org/video/derf/ (2006). Xiph.org test media2. Ancuti, C.O., Ancuti, C., Hermans, C., Bekaert, P.: Layer-based

single image dehazing by per-pixel haze detection. In: ACM SIG-GRAPH ASIA 2010 Sketches, SA ’10, pp. 45:1–45:2 (2010)

3. Baker, S., Roth, S., Scharstein, D., Black, M.J., Lewis, J.,Szeliski, R.: A Database and Evaluation Methodology for Opti-cal Flow (2007)

4. Bolz, J., Farmer, I., Grinspun, E., Schröoder P.: Sparse matrixsolvers on the GPU. ACM Trans. Graph. 22, 917–214 (2003)

5. Black, M.J., Anandan, P.: The robust estimation of multiple mo-tions: parametric and piecewise-smooth flow fields. Comput. Vis.Image Underst. 63(1), 75–104 (1996)

6. Chen, J., Tang, C.K.: Spatio-temporal markov random field forvideo denoising. In: IEEE Conference on Computer Vision andPattern Recognition (2007)

7. Chuang, Y.Y., Agarwala, A., Curless, B., Salesin, D.H.,Szeliski, R.: Video matting of complex scenes. In: SIGGRAPH(2002)

8. Dong, X.M., Hu, X.Y., Peng, S.L., Wang, D.C.: Single color im-age dehazing using sparse priors. In: 2010 17th IEEE InternationalConference on Image Processing (ICIP), pp. 3593–3596 (2010)

9. Fattal, R.: Single image dehazing. ACM Trans. Graph. 27(3), 1–9(2008)

10. Haussecker, H.W., Fleet, D.J.: Computing optical flow with phys-ical models of brightness variation. IEEE Trans. Pattern Anal.Mach. Intell. 23, 661–673 (2001)

11. He, K., Sun, J., Tang, X.: Single image haze removal using darkchannel prior. In: IEEE Conference on Computer Vision and Pat-tern Recognition, pp. 1956–1963 (2009)

12. He, K., Sun, J., Tang, X.: Guided image filtering. In: EuropeanConference on Computer Vision (2010)

13. Kopf, J., Neubert, B., Chen, B., Cohen, M., Cohen-Or, D.,Deussen, O., Uyttendaele, M., Lischinski, D.: Deep photo: model-based photograph enhancement and viewing. ACM Trans. Graph.27(5) (2008)

14. Koschmeider, H.: Theorie der horizontalen sichtweite. Beitr. Phys.D. Freien Atm., 171–181 (1924)

15. Lin, S.Y., Shi, J.Y.: A Markov random field model-based approachto natural image matting. J. Comput. Sci. Technol. 22 (2007)

16. Lv, X., Chen, W., Shen, I.F.: Real-time dehazing for image andvideo. In: Pacific Conference on Computer Graphics and Applica-tions, pp. 62–69 (2010)

17. Narasimhan, S.G., Nayar, S.K.: Chromatic framework for visionin bad weather. In: Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition, vol. 1,pp. 598–605 (2000)

18. Preetham, A.J., Shirley, P., Smits, B.: A practical analytic modelfor daylight. In: ACM SIGGRAPH (1999)

19. Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Instant dehazingof images using polarization. In: Proceedings of the IEEE Com-puter Society Conference on Computer Vision and Pattern Recog-nition, Kauai, HI, United States, vol. 1, pp. I325–I332 (2001)

20. Shwartz, S., Namer, E., Schechner, Y.Y.: Blind haze separation.In: Proceedings—Conference on Computer Vision and PatternRecognition, CVPR 2006, vol. 2, pp. 1984–1991 (2006)

21. Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimationand their principles. In: IEEE Conference on Computer Vision andPattern Recognition (2010)

22. Tan, R.T.: Visibility in bad weather from a single image. In: IEEEConference on Computer Vision and Pattern Recognition (2008)

23. Tarel, J.P., Hautière, N.: Fast visibility restoration from a singlecolor or gray level image. In: Proceedings of IEEE InternationalConference on Computer Vision, Kyoto, Japan, 2009, pp. 2201–2208 (2009)

24. Villegas, P., Marichal, X.: Perceptually-weighted evaluation crite-ria for segmentation masks in video sequences. IEEE Trans. ImageProcess. 13(8), 1092–1103 (2004)

25. Xu, L., Jia, J., Matsushita, Y.: Motion detail preserving opticalflow estimation. In: IEEE Conference on Computer Vision andPattern Recognition (2010)

26. Zhang, G., Jia, J., Wong, T.T., Bao, H.: Recovering consistentvideo depth maps via bundle optimization. In: IEEE Conferenceon Computer Vision and Pattern Recognition (2008)

27. Zhang, J., Li, L., Yang, G., Zhang, Y., Sun, J.: Local albedo-insensitive single image dehazing. Vis. Comput. 26, 761–768(2010)

Jiawan Zhang received the M.Sc.and Ph.D. degrees in Computer Sci-ence from Tianjin University in2001 and 2004, respectively. Cur-rently, he is Associate Professor atthe School of Computer Softwareand Adjunct Professor at the Schoolof Computer Science and Technol-ogy, Tianjin University. His mainresearch interests are visual analyt-ics, medical image processing, andimage synthesis. He is a member ofthe ACM and IEEE.

Liang Li received the B.Sc. degreeat the School of Computer Scienceand Technology from Dalian Uni-versity of Technology, P.R. China,in 2008. He is currently workingtowards the Ph.D. degree in Com-puter Application at the MTS Labo-ratory, Tianjin University. His mainresearch interests include computervision and information visualiza-tion.

Yi Zhang received M.Sc. and Ph.D.degrees in Computer Science fromTianjin University in 2006 and 2009,respectively. She is currently a lec-turer in the School of ComputerSoftware, Tianjin University. Herresearch interests include imageediting techniques, virtual heritagerestoration and information visual-ization.

Video dehazing with spatial and temporal coherence 757

Guoqiang Yang Guoqiang Yangreceived the B.Sc. degree at theSchool of Computer Science andTechnology from Hebei Universityof Technology, P.R. China, in 2008.He is currently working towards theM.Sc. degree in Computer Applica-tion at the MTS Laboratory, TianjinUniversity. His main research inter-ests include computer vision and in-formation visualization.

Xiaochun Cao received the B.E.and M.E. degrees, both in Com-puter Science, from Beihang Uni-versity (BUAA), Beijing, China.He received the Ph.D. degree inComputer Science from the Uni-versity of Central Florida, Orlando,USA, with his dissertation nomi-nated for the university-level awardfor Outstanding Dissertation. Aftergraduation, he spent about two andhalf years at ObjectVideo Inc. asa Research Scientist. Since August2008, he has been with Tianjin Uni-

versity, China, where he is currently a Professor of Computer Sci-ence. He has authored and co-authored over 40 peer-reviewed journalsand conference papers, and has been in the organizing and the techni-cal committees of several international colloquia. In 2004 and 2010,Dr. Cao was the recipients of the Piero Zamperoni best student paperaward at the International Conference on Pattern Recognition.

Jizhou Sun Jizhou Sun receivedthe M.Sc. degree from Tianjin Uni-versity, China, in 1985 and thePh.D. degree from Sussex Uni-versity, U.K. in 1994. He is nowa Professor in Computer Scienceand Technology in Tianjin Univer-sity. His research interests includecomputer architecture, distributedand parallel computing, computergraphics process, virtual reality andnetwork security.