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Research Article

Contrast enhancement for images in turbid water
HUIMIN LU,1,2,3,* YUJIE LI,1 LIFENG ZHANG,1 AND SEIICHI SERIKAWA1
1

Department of Electronic and Electrical Engineering, Kyushu Institute of Technology, Kitakyushu 8048550, Japan
State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, Shanghai 200240, China
3
State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China*Corresponding author: luhuimin@ieee.org
2

Received 10 December 2014; revised 11 March 2015; accepted 14 March 2015; posted 17 March 2015 (Doc. ID 229144); published 23 April 2015

Absorption, scattering, and color distortion are three major degradation factors in underwater optical imaging.
Light rays are absorbed while passing through water, and absorption rates depend on the wavelength of the light.
Scattering is caused by large suspended particles, which are always observed in an underwater environment. Color
distortion occurs because the attenuation ratio is inversely proportional to the wavelength of light when lightpasses through a unit length in water. Consequently, underwater images are dark, low contrast, and dominated bya bluish tone. In this paper, we propose a novel underwater imaging model that compensates for the attenuation
discrepancy along the propagation path. In addition, we develop a robust color lines-based ambient light estimator and a locally adaptive filtering algorithm for enhancing underwater images in shallow oceans. Furthermore,
we propose a spectral characteristic-based color correction algorithm to recover the distorted color. The enhanced
images have a reasonable noise level after the illumination compensation in the dark regions, and demonstrate an
improved global contrast by which the finest details and edges are enhanced significantly. © 2015 Optical Societyof America
OCIS codes: (100.2980) Image enhancement; (110.0113) Imaging through turbid media.
http://dx.doi.org/10.1364/JOSAA.32.000886

1. INTRODUCTION
Currently, autonomous underwater vehicles (AUVs) andremotely operated vehicles (ROVs) are commonly used forunderwater object recognition. In the past two decades, sonarhas been widely used to detect and recognize objects in underwater environments. However, for short-range object recognition, vision sensors must be used instead of sonar because sonaryields low-quality images [1].
In underwater observation, large suspended particles in turbid water cause scattering. The underwater optical images thus
suffer from poor visibility because of the medium. Additionally,color distortion occurs because different wavelengths are attenuated to different degrees in water. Consequently, captured
images of underwater environments are dominated by a bluishtone. The absorption of light in water substantially reduces its
intensity. In fact, objects at distances of more than 10 m from thecamera are almost indistinguishable [2].
Research in this field has long focused on color correction
and descattering methods. Schettini and Corchs [2] reviewed
methods for image enhancement and restoration, as well as
subjective and quantitative assessment indices. Integrated color
models were proposed for hyper-spectral correction in [3].
These models typically involve multi-step approaches thatuse color histogram stretching and color correction to account1084-7529/15/050886-08$15/0$15.00 © 2015 Optical Society of America

for varying depths and the use of natural or artificial lighting.
Distance-based contrast and color degradation models haverecently been generated for turbidity images to recover underwater scenes [4–7].
In this paper, we focus on a single image enhancement
method. Single image enhancement is a challenging, but illposed problem. The obtained brightness of each pixel depends
on the observed scene point radiance, scattering, attenuation,
and ambient illumination. In reviewing recent studies, wefound that research related to underwater image enhancement
can be classified into the following six main categories.
(1) Polarization: Schechner and Karpel [8] restored images
taken at significantly varied scene distances by using a polarization filter attached to a camera. A distance map of the scene
was obtained by capturing two polarized images from different
angles. Liang et al. [9] proposed a Stokes matrix-based fourangle rotation polarization filter to remove haze. However,these methods cannot attenuate the transmission of radiancethrough polarization filters or multiple exposures, especiallyfor time variations that are adverse to visibility and illumination
conditions.
(2) Turbidity medium: Narasimhan and Nayar [10]and Cozman and Krotkov [11] analyzed a static scene byobtaining multiple images under different visibility conditions.
Although they reported impressive results, a static camera and aVol. 32, No. 5 / May 2015 / Journal of the Optical Society of America A

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significant change in media turbidity were required under constant illumination conditions.
(3) Multi-lighting: Narasimham et al. [4] and Tsiotsios
et al. [12] proposed the use of multiple lights to estimate thebackscatter from a scene. Treibitz and Schechner [13] proposed
fusing images obtained using two-directional illumination tocreate a single clearer image. However, it is difficult to recover
the time variations adverse to visibility in the presence of floating turbidity sediments.
(4) Scene depth: Studies have calculated scene depth tosolve the ambiguities in the visibility enhancement problem[14,15]. These works made assumptions about the scene that necessitate either manual inputs or special structures. Generating
this information is difficult in water.
(5) Fusion: Ancuti et al. [16] combined the Laplacian contrast, contrast, saliency, and exposure features of whitebalanced and color-corrected images. They then utilized theexposure fusion algorithm to obtain the final result. However,
several results were color shifted because of the exposure process;selecting the exposed images is difficult.
(6) Prior: Fattal [17] estimated scene radiance and derived
a transmission image using single-image statistics. He et al. [18]analyzed numerous natural sky images and determined thatmost color images contain a dark channel. On the basis of thisfinding, they proposed a dark channel prior dehazing algorithm. The subsequent image enhancement resulted in regional
contrast stretching that could cause halos or aliasing. Moreover,the dark channel assumption is based on natural outdoor image
statics that might not hold in arbitrary underwater scenes.
In underwater environments, captured images are significantly influenced by the medium [19]. In the present paper,
we propose a novel shallow ocean imaging model and a corresponding enhancement algorithm. First, we propose a robustcolor line to estimate the ambient light. Next, we estimate
the transmission through an underwater median dark channel
prior (UMDCP). After that, we develop a locally adaptive crossfilter (LACF) to remove the scatter. Finally, color correction is
performed according to the spectral properties.

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the light reflection rate is ρc x. In accordance with the
Koschmieder model [21], the scene image I c x formed at
the camera can be formulated as
I c x  E cA x · N rer cDx  E cL x · N rer cd x  · ρc x× T c x  1 − T c xAc ;c ∈ fr; g; bg;(3)cwhere the inhomogeneous background A represents the ambient light and T c x is the transmission map.
Equation (3) incorporates the light scattered during propagation from the object to the camera through a distance d x
and a scene depth Dx. Once the scene depth Dx, homogeneous background Ac , and object–camera distance d x are
known, a clean image can be recovered.
As indicated in Eq. (3), we suppose the light J c x reflectedfrom point x is
J c x  E cA x · N rer cDx  E cL x · N rer cd x  · ρc x
c ∈ fr; g; bg:(4)Then, Eq. (3) can be rewritten as
I c x  J c xT c x  1 − T c xAc ; c ∈ fr; g; bg:(5)

The underwater image formation model in Eq. (5) takes
into consideration the scattering effect, attenuation, and artificial lighting. Thus, we consider taking the UMDCP to remove
the scattering. Meanwhile, we also consider the use of spectral
characteristics to recover the underwater scene color. Figure 1
shows a schematic of the proposed model. To improve theimage quality, we consider the processing flowchart shownin Fig. 2. In Fig. 2, we first adjust the white balance and apply
a bilateral filtering-based deflicker filter. Next, we estimate theambient light using color lines. The coarse transmission is thenestimated by using UMDCP. Then, we use the proposed
locally adaptive cross filter to refine the transmission. Oncethe transmission map is obtained, we can remove the scatter.
Finally, we restore the scene color by using the proposed camera spectral characteristic-based color correction algorithm.
3. DESCATTERING AND COLOR CORRECTION

2. UNDERWATER LIGHT PROPAGATION
MODEL

A. Robust Ambient Light Estimation

Artificial and atmospheric light are sources of illumination in ashallow ocean environment. Suppose the intensity of light at
point x is W . The illumination can be formulated using theenergy attenuation model as follows:
E cW x  E cL x  E cA x;c ∈ fr; g; bg;

E cW x is the amount of illumination,(1)

The ambient light Ac in Eq. (5) is often estimated as the brightest color in an image [16,22–24]. However, for underwater
imaging, because artificial lighting and flashing are often used,some objects (e.g., flickers, highlights) are brighter than theambient light [25]. The traditional approaches for estimating
the ambient light may produce undesired results. To reliably

E cA x is thewhereamount of illumination of atmospheric light at point x,
E cL x is the illumination of artificial light at point x, andN rer is the normalized residual energy ratio [20]. In turbid
water, N rer has the following values:(
0.75 ∼ 0.85 if λ  650 ∼ 750 μmred
N rer c  0.86 ∼ 0.95 if λ  490 ∼ 550 μmgreen (2)0.95 ∼ 0.99 if λ  400 ∼ 490 μmblue.
We assume the distance between the artificial light and theobject is d x, and Dx is the underwater scene depth.
Absorption and scattering occur in this process. Suppose that

Fig. 1. Schematic of the shallow ocean imaging model.

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functions of only the diffuse reflectance and are independentof objects, the SU V matrix is converted to a linear matrix.
The color distance ρ is defined as
qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ρ  I cU x2  I cV x2 :(6)
The angle θ can be obtained with I cU x and I cV x.
According to the component I cS x and the color distance ρ,
we can remove the spatial coordinate φ. Then, we take thediffusion operation as
φ 0  maxmaxφ  BFmaxmaxφ; θ∕ maxmaxθ;(7)
where BF represents the bilateral filter [29]. Finally, we cancalculate the ambient light Ac using the image with highlights
removed, as in [27].
B. Coarse Transmission Estimation

Fig. 2. Summary of the proposed approach for underwater image
enhancement.

estimate the ambient light, Kim et al. [26] proposed a hierarchical searching method. This method is based on quadtree
subdivision. For each rectangular region, the minimum distance between the brightest pixel (255, 255, 255) and the region is estimated. This method can be used to find the brightest
region in the image. However, it may also produce undesired
results if the brightest pixels are objects. Fattal [27] proposed a
global regularity approach based on color lines. However, inseveral cases, if the image contains a single surface albedo orrich details, this method results in color shifts.
To solve these problems, we propose a robust ambient lightestimation method. First, we remove the highlighted regions
by applying a bilateral filter-based deflicker filter. Unlike thehighlight removed in [28] from specular highlights in naturalscenes, the highlights we removed are with consideration forlight compensation.
We first transferred the input image to SU V space. The S
channel encodes the entire specular component and a fractionof the diffuse component. As the U and V channels are

According to [23], the red channel is the dark channel of underwater images. In our experiments, we found that the lowest
pixel value of the RGB channels in turbid water is not always
the red channel, but is occasionally the blue channel. Although
the light of red wavelength is absorbed easily when it propagatesin water, the distance between the camera and object is not
sufficient for light of red wavelength to be significantly absorbed (Fig. 3). Additionally, the blue channel is absorbed theleast. Consequently, in this paper, we use a dual channel (redand blue) to estimate the coarse transmission map.
The median filter was first proposed by Tarel and Hautiere[30] and was used for natural image dehazing. In [24], its use
was extended to underwater imaging.
As previously mentioned, we found that turbid underwater
images exhibited a mostly dark image d˜x. The median operation is suitable for reducing the halo effect when estimating
the coarse transmission. Thus, the underwater median dark
channel priors can be defined by
I c x˜;c ∈ fr; bg;(8)d x  median minc∈fr;bg Ac
Rm;n
where R is a square window of size 5 × 5. For each pixel locatedat position m; n in the square patch R, the values from the redand blue channels are compared, and the lower value is selected.
The proposed method can prevent the halo effect aroundocclusion boundaries. Accordingly, the coarse estimate of transmission is obtained using

Fig. 3. RGB histogram of an underwater image.

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Research Articled x  1 − ωd˜x;(9)where ω  0.9 for most scenes.
C. Locally Adaptive Cross Filter-Based Descattering

We roughly estimated the coarse transmission d x in Section
B. However, its transmission contains mosaic effects and yields
less accurate results. Therefore, we have developed a locally
adaptive cross filter to reduce the mosaic effects.
The use of a median filter has been considered an effective
method for removing outliers. However, the traditional medianfilter usually results in morphological artifacts, such as rounded
corners. To solve this problem, a weighted median filter has
been proposed [31,32]. The weighted median filter can becomputed from the local histograms hx; · byX
hx; i W x; yδV y − i;(10)y∈N x

where W · corresponds to the weight assigned to pixel y inside
a local window N x centered at the corresponding pixel x, i isthe discrete bin index (usually i  16), and δ is the Kronecker
delta function. The weight W x; y depends on the image x,
which can differ from the pixel value V . Consider it to be instead x by coarse transmission d x; y is the white-balanced
image I cB x. Finally, the refined transmission is produced byX
W d x; I cB xδV I cB x − i; (11)hd x; i 
x∈N x

where W · is the normalized convolution domain filter(NCDF) [33]. For the uniform discretization DΩ of theoriginal domain Ω, NCDF can be written asX
W x 0   1∕K x 0 d xH td x̂ 0 ; tI cB x̂; (12)Px∈DΩ

where K x 0  x∈DΩ H td x̂ 0 ; tI cB x̂ is a normalization factor for x 0 , and t·0  ct·. t represents the isometrictransform, and ct· is a domain transform [33]. Using theefficient moving average approach to apply normalized convolution with a box filter, the box kernel is
H td x̂ ; tI cB x̂  δB fjtd x̂ 0  − tI cB x̂j ≤ rg;

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obtain the reflectivity ρc x. For this, we take the least squares
solution,ρc x  J c xT · J c x−1 · J c xT
· E cA x · N rer cDx  E cI x · N rer cd x : (16)After removing the effects of ambient light and increasing
the distance d x between the object and the camera, the scattering can be removed by subtracting the artificial light term in
Eq. (4), since our goal is to obtain E cA x · N rer cDx in
Eq. (4). In actual imaging, we can obtain the depth Dx using
a pressure sensor. Thus, we can recover the absorption recovered image using Eq. (3).
E. Color Correction

A simple color correction method was proposed in [23]. In
practice, the spectral response function of a camera also affects
the colors. In this paper, we use the chromatic transfer function
τλ to measure the light from the surface to a given object
depth as
τλ 

E Sλ
;
EU
λ

(17)

where the transfer function τλ at wavelength λ is derived fromthe irradiance of the surface E sλ using the irradiance of theunderwater object E U
λ . On the basis of the spectral responseof the RGB camera, we convert the transfer function to the
RGB domain as
Z 750 nmτλ · S c λ; c ∈ fr; g; bg;(18)
τRGB 
λ400 nmwhere τRGB is the weighted RGB transfer function and Scλ isthe continuous underwater spectral characteristic function forcolor band c. Suppose the nonabsorption image is Ĉx 
E cA x · N rer cDx . Finally, the corrected image is obtained
from the weighted RGB transfer function usingCx  Ĉx · τRGB ;(19)

where Cx is the color-corrected image.

0

(13)
pﬃﬃﬃ
where r  σ H 3 is the filter radius, δB is a Boolean functionthat equals 1 when its argument is true and 0 otherwise, and σ H
is the standard deviation of the coarse transmission map.
Finally, once the transmission d x is known in Eq. (11),the transmission map T c x is dependent on the extinction coefficient β of the medium as
T c x  e −βd x :(14)

The descattered image J x can be obtained by Eq. (5),c

J c x 

I c x − 1 − T c xAc;
maxfT c x; εg(15)

where the definition ε  0.001 is given to prevent division
by 0.
D. Absorption Compensation

In Section C, we obtained the transmission map T λ x and thedescattered image J c x. To remove absorption effects, we must

4. EXPERIMENTAL RESULTS
Thirty underwater images were selected, including 15 images
from the Internet and 15 images from our water tank experiments. The water tank contains 180 L water (90 cm × 45 cm×
45 cm). Both the underwater lights (INON LEs) and theunderwater camera (OLYMPUS μTough TG2) were placedin the water. The objects were placed 30 cm deep. The distance
between the objects and the camera was approximately 60 cm.
We executed the proposed algorithm on the selected images.
The performance of the proposed algorithm is evaluated bothanalytically and experimentally by using ground truths. We also
compare the proposed method with other currently proposed
state-of-the-art methods. The results demonstrate that the
proposed method shows superior scatter removal and color correction capabilities.
As the first evaluation, the performance of the proposed
method is compared with other methods in terms of the ability
to remove scatter in water. Figure 4 illustrates the results ofdescattering an example image using different methods. The

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method proposed by He et al. [22] produces a result comparable to our proposed method in regions with heavy scatter.
However, the use of Laplacian matting to overcome increases
in depth is time consuming. Furthermore, in this method, theambient light is simply calculated as 0.1% of the brightness ofthe brightest pixels, which led the resulting images to containsome hazes.
The method proposed by Tarel and Hautiere [30] is a filterbased approach that estimates the veil by applying a median filter. The merit of this method is its linear complexity, which canbe implemented in real time. However, it also tends to underestimate the transmission and produce an oversaturated result.
The double Wiener filtering-based approach proposed by
Gibson et al. [24] can automatically refine the transmission
using a locally adaptive Wiener filter. This method canalso be implemented in real time. However, it estimates theambient light using a dark channel prior, which may produce

Research Article

an undesirable result. The resulting image also contains somescatter and color shifts.
Fattal [27] proposed the color line-based method can better
estimate the ambient light. However, several cases show thatoverestimated ambient light will cause color shifts. Because
of incorrectly estimating the ambient light, the result in
Fig. 4(e) is the darkest. As illustrated in the zoomed-in resulting
images shown in Figs. 4(g)–4(k), the proposed method demonstrates the best performance of all the methods. Less scatterand fewer halos remain when using the proposed method.
Moreover, the proposed method tends to more clearly represent
object details. The methods proposed by He et al. andTarel et al. do not correctly estimate the transmission; themethod proposed by Fattal et al. overestimates the transmission; and the method proposed by Gibson et al. method causes
transmission jumps. As shown in Figs. 4(l)–4(p), our transmission is much clearer than those of the other methods.

Fig. 4. Comparison of the presently proposed method with methods proposed by He et al., Tarel et al., Gibson et al., and Fattal et al. (a) Input
image. (b)–(f) Results of methods by He et al., Tarel et al., Gibson et al., and Fattal et al.; and results of the proposed method. (g)–(k) Zoomed-in
images of results shown in (b)–(f), respectively. (l)–(p) Transmission used in methods depicted in (b)–(f), respectively.

Fig. 5. Comparison of ambient light areas estimated using methods proposed by He et al., Tarel et al., Gibson et al., Fattal et al., and that of thepresently proposed method.

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In Fig. 5, we illustrate the estimated ambient light areas of
the image with different methods. The estimated ambient lightof the methods proposed by He et al., Tarel et al., Fattal et al.,and Gibson et al. appear unsatisfactory. Some of the objects

891(e.g., fish, coral reefs) are estimated to be ambient light. In contrast, because our proposed method initially removes the highlighting effect, the desired ambient light can be correctly
estimated.

Fig. 6. Toys and cups. (a)–(d) Input images. Results with methods proposed by (e)–(h) He et al., (i)–(l) Tarel et al., (m)–(p) Gibson et al., and(q)–(t) Fattal et al.; and (u)–(x) with the presently proposed method.

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Figure 6 illustrates the simulation results obtained using thedifferent methods. In this experiment, we made a linear scale ofeight turbidity steps ranging from clean to heavily scattered byadding deep sea soil to the seawater (from 0.6 to 100 mg∕L).
Some haze remained in the resulting images using the methods
proposed by He et al. and Gibson et al., while those using themethod proposed by Tarel et al. and Fattal et al. show color
distortion. As shown in Figs. 6(u)–6(x), our method effectively
removes haze and correctly recovers color.
In addition to visual analysis, we conducted a quantitative
analysis, first from a statistical perspective and using the statistical parameters of the images. We calculated the contrast-tonoise ratio (CNR) [34], peak signal-to-noise ratio (PSNR), andstructural similarity index (SSIM) of images obtained using
each method [35]. The CNR is similar to the signal-to-noise
ratio (SNR) and robust in measuring hazy images. The valueof the CNR [34] is between 0 (worst) and 100 (best). The
SSIM index determines the structural similarity between two
images. The value of SSIM is between 0 (worst) and 1 (best).
Figure 7(a) shows the CNR values of the selected 30 images
obtained using the different methods. The CNR values demonstrate that our method performs better than other methods.
However, in several cases, if the contrast of the objects was

Research Article

Table 1. SSIM Values of Different Methods with Known
Ground Truths
MethodGibsonFattalHe

Tarel

Proposed

Image 1
Image 2
Image 3
Image 40.72800.73500.74670.74640.64400.29920.29750.27440.76860.74000.74950.73670.75860.68480.71060.71030.77130.79950.81520.7106

small, a failed result was obtained. Figure 7(b) illustrates the
PSNR values of the different methods using four images withknown ground truths. The PSNR value of Image 1 is lower
because of the heavy illumination, meaning the difference
between the illumination of the objects and the backgroundis very low. Table 1 lists the measured SSIM values of several
images. These results indicate that our approach works well for
scatter removal.
5. DISCUSSION AND CONCLUSION
In this paper, we have explored and successfully implemented
novel enhancement techniques for underwater optical images.
We proposed a physical underwater dark channel prior. To
correctly estimate the ambient light, we develop a robust colorline-based ambient light estimator. We also introduced aweighted guided domain filter to compensate for the transmission. The proposed filter has the benefits of preserving edges,
removing noise, and reducing computation time. Moreover,the proposed underwater image color correction method
successfully reconstructed colorful underwater images that are
better than the images produced using current state-of-the-art
methods. Our experiments showed that the proposed methods
are suitable for underwater optical imaging.
In our experiments, we found that, as the amount of
turbidity sediment increases, image contrast decreases. This
may result in the inability to accurately estimate ambient light.
Another issue is that the proposed method can only remove
haze-like scatter. In future work, we need to design novel algorithms for large particles scatter removal.
Grant in Aid for Research Fellows of Japan Society for the
Promotion of Science (13J10713); Grant in Aid for
Foreigner Research Fellows of Japan Society for the
Promotion of Science (P15077); Open Research Fund ofState Key Laboratory of Marine Geology in Tongji
University (MGK1407); Open Research Fund of State Key
Laboratory of Ocean Engineering in Shanghai Jiaotong
University (OEK1315).
The authors wish to thank Japan Agency for Marine-Earth
Science and Technology (JAMSTEC) for offering the test
underwater images.
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Fig. 7. (a) Comparison of CNR values of different methods.
(b) Comparison of PSNR values of different methods using four
images with known ground truths.

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