Simultaneous Underwater Visibility Assessment, Enhancement andImproved Stereo
Martin Roser1 , Matthew Dunbabin2 and Andreas Geiger1,3
Abstract— Vision-based underwater navigation and obstacle
avoidance demands robust computer vision algorithms, particularly for operation in turbid water with reduced visibility.
This paper describes a novel method for the simultaneous underwater image quality assessment, visibility enhancement anddisparity computation to increase stereo range resolution underdynamic, natural lighting and turbid conditions. The techniqueestimates the visibility properties from a sparse 3D map of
the original degraded image using a physical underwater lightattenuation model. Firstly, an iterated distance-adaptive image
contrast enhancement enables a dense disparity computationand visibility estimation. Secondly, using a light attenuation
model for ocean water, a color corrected stereo underwater
image is obtained along with a visibility distance estimate.
Experimental results in shallow, naturally lit, high-turbidity
coastal environments show the proposed technique improves
range estimation over the original images as well as image
quality and color for habitat classification. Furthermore, therecursiveness and robustness of the technique allows implementation onboard an Autonomous Underwater Vehicle for
improving navigation and obstacle avoidance performance.

I. I NTRODUCTION
Autonomous Underwater Vehicles (AUVs) are increasingly being utilized as tools to obtain visual observationsof benthic habitats in highly unstructured and dynamic
environments. These include shallow coral reefs, near-shoremangroves and marinas with degraded visibility making
traditional diver surveys impractical and dangerous. The
surveys, as considered here, typically consist of low-altitude(0.5 to 2 m above the seafloor) transects, similar to divervideo transects, to allow habitat classification and mapping.
However, the ability to discriminate objects and perform
classification from underwater images, whether collected bydivers, AUVs, or Remote Operated Vehicles (ROV) depends
on the visibility. Visibility in this paper means the distancewhich we can reliably discriminate an object in the scene.
In shallow coastal environments the visibility can be lessthan 1 m in tidal embayments and up to 10-20 m withoceanic water. Whilst most literature referenced throughout
this paper has considered image enhancement in relatively
clear oceanic water, the focus of this paper is the significantly
more challenging naturally lit turbid coastal environments.
1 Dept. of Measurement and Control, Karlsruhe Institute of Technology,
76131 Karlsruhe, Germany roser@kit.edu
2 Institute for Future Environments, Queensland University of Technology,
Brisbane, Queensland, Australia m.dunbabin@qut.edu.au
3 Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany

andreas.geiger@tue.mpg.de
The authors would like to thank the CSIRO Sensors and Sensor Network
Transformational Capability Platform, the Karlsruhe School of Optics andPhotonics, and the Karlsruhe House of Young Scientists for their financial
support to conduct this project.

A. Related Work
There has been a long history of research to enhance underwater images focusing on color restoration and dehazing.
A comprehensive overview of methods for image enhancement and restoration along with subjective and quantitative
assessment metrics is given in [1]. A particular problem
with underwater images is that of color consistency and appearance changes due to preferential absorption of different
color wavelengths as they travel through the water [2], [3].
Color techniques ranging from integrated color models [4]to hyper-spectral correction [5] have been proposed. These
are typically multi-step approaches utilizing color histogram
stretching and color correction to account for varying depth
and natural or artificial illumination [4]. Distance-dependent
contrast and color degradation [2] – which is the case inturbid media – is typically only addressed in relatively low
turbidity images, although several works have achieved scene
depth recovery and color enhancement [6]–[8].
Kocak et al. [9] provides an overview of improving longrange visibility in degraded images highlighting additional
techniques such as structured and multi-source lighting.
These methods can produce terrain maps in real-time but
their depth of field can be limited.
Single image visibility assessment and enhancement isstill a challenging and ill-posed problem. The observedbrightness of each pixel depends on the observed scene point
radiance, the scattering and attenuation properties of thecircumfluent medium, the scene point distance as well as theambient illumination. Related work for image enhancement(often termed dehazing) can be classified into four maincategories depending on the additional information used for
disambiguation.
1) Polarization: Schechner et al. [10], [11] consider significantly varying scene distance image restoration using apolarizer attached to a camera. By taking two or more images
with different degrees of polarization (DOP), the authors areable to nearly double the visible range and through image
processing derive a distance map of the scene. Howeverpolarization filters attenuate the radiance transmission to thecamera which is undesirable, particularly with time-varying
adverse visibility and illumination conditions.
2) Turbidity: Narasimhan and Nayar [12], [13] as well
as Cozman and Krotkov [14] analyse a static scene bytaking multiple images under different visibility conditions.
Even though they report impressive results it requires astatic camera and a significant change in media turbidity forconstant illumination conditions.

3) Scene Depth: Performing image enhancement utilizing
the estimated scene depth (e.g. with a stereo camera system)
is a chicken-and-egg dilemma. Contrast degradation leads toa low signal-noise-ratio (SNR) which makes disparity computation difficult. Conversely accurate and dense scene depth
is a prerequisite for solving the ambiguities in the visibility
enhancement problem. Whilst no literature on underwater
stereo visibility enhancement has been found, some studies
have considered it for seeing through fog [15]–[17]. These
works make assumptions about the scene requiring either
manual input or structure such as road or uniform georeferenced terrain which is difficult to achieve underwater.
4) Priors: Single image underwater visibility enhancement often considers priors from image statistics. Fattal [18]and Tan [19] derive assumptions from different phenomena
in order to develop a cost function in the framework ofMarkov random fields (MRFs). He et al. [20] derive a Dark
Channel Prior from natural image statistics. This postulates
that there will be a low response in at least one color
channel for haze-free images. Turbid media scatter ambient
light towards the observer and lead to an additive signal
which is a function of the scattering coefficient and thescene distance. The subsequent image enhancement becomesa regional contrast stretching which can create halos. Since
the ambiguity between visibility and scene distance cannot
be solved, their approach still yields one unknown (depth
scale). In addition the dark channel assumption is basedon natural outdoor image statistics which might not holdin arbitrary underwater scenes particularly with man-madeinfrastructure. Notable works utilising the Dark Channel
Prior include MRFs for estimating scene radiance variation[8], [21] and median filters [22] for generating depth maps todehaze and color correct underwater images with relatively
small amounts of haze.
B. Paper Outline
The remainder of the paper is as follows. Section II
describes the proposed underwater visibility enhancement
and assessment approach. Section III presents a simulation
evaluation of the proposed approach, with Section IV evaluating the methodology using experimental field data collectedby an AUV. Finally, Section V summarizes the paper anddescribes future research directions.
II. U NDERWATER V ISIBILITY E STIMATION & I MAGEE NHANCEMENT
The goal of this research is to allow real-time enhancementof underwater images which are naturally lit and degraded
due to relatively high turbidity and other visibility reducing
phenomena. Enhancement of underwater images requires
modelling and estimation of the water absorption and scattering characteristics to remove haze. However it also requiresa scene depth map. Many papers use a single image andthe dark channel prior in the estimation of a depth map [8],[21], [22]. In our approach, we use stereo images in a twostage enhancement process to improve overall image quality allowing visibility and range estimation. The following

Fig. 1. Underwater Light Model. Light directly transmitted from the scene
point will be wavelength dependent exponentially attenuated over distanced and superimposed by the ambient illumination I∞ at depth z that will
be refracted towards the observer.

subsections describe the modelling and estimation steps indetail, with the overall image enhancement approach detailedin Section II-E.
A. Underwater Light Propagation Modelling
Underwater light models generally follow a standard attenuation model [6] to accommodate wavelength attenuation
coefficients [2]. In this study the Koschmieder Model [23]was adopted which has been established as a description of
the atmospheric effects of weather on the observer [10], [12],[16], [24]. In outdoor clear weather conditions, the radiancefrom a scene point would reach the observer nearly unaltered.
However when imaging underwater the irradiance observed
by each pixel of the camera (E) is linear combinationof directly transmitted scene object radiance that will beattenuated in the line of sight and scattered ambient lighttowards the observer as depicted in Figure 1. Therefore, theKoschmieder model has been adapted here for underwater
lighting conditions (as in [2], [3]) and can be expressed as
E = I∞ (z)ρe−β(λ)d + I∞ (z)(1 − e−β(λ)d ),(1)

where I∞ (z) is the ambient light at depth z, ρ is thenormalized radiance of a scene point, d is the distance fromthe scene point to the camera and β(λ) is the total beam
attenuation coefficient which is nonlinear and dependent on
the wavelength λ. The ambient illumination at depth z itself
is subject to light attenuation in the following form:
I∞ (z) = T I0 e−β(λ)z ,(2)where I0 is the atmospheric intensity at the surface, and T
is the transmission coefficient at the water surface.
When dealing with atmospheric effects, like fog or haze,
β is normally assumed to be constant for the whole visible range. However, in underwater applications different
wavelengths of light (λ) are nonlinearly and preferentiallyabsorbed relative to others. According to [3], the total beam
attenuation coefficient in saltwater can be approximated assw
β(λ) ≥ Kw(λ) + bp (λ) + ap (λ) + ay (λ)(3)

sw
where Kw
is the diffuse attenuation coefficient for theclearest saltwater, bp and ap are the scattering and absorption
coefficients of particles respectively, and ay is the absorption
coefficient of dissolved organic material. Figure 2 illustrates
the wavelength dependency of β even in pure saltwater

1

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[nm]

Fig. 2. Clear ocean water light attenuation characteristics deduced from[3]. Also illustrated is typical imaging sensor sensitivity and its overlap
with attenuation coefficients which results in a mean channel chromaticity
change over depth.

with the highest visibility (bp = 0, ap = 0, ay = 0). Thisphenomena leads to a depth-dependent image chromaticity
change.
However, in real ocean waters, the scattering and absorption coefficients are non-negligible, particularly in the highly
turbid water scenarios considered in this paper. Therefore,
these coefficients need to be approximated from the collected
images as discussed in the following sections.
B. Estimating Visibility Coefficients
Estimating the visibility conditions (I∞ (z), ρ, β(λ) andd) directly from single images is a highly ill-posed problem.
For a color image with N pixels, we achieve N observable
intensity values in each image color channel E c where c ∈{R, G, B}. However, the estimation problem in (1) yields(c + 1) · N + 2c > c · N unknowns for each image.
For this reason it is assumed a known 3D scene model togive d (the distance to each point in the image) which canbe obtained to varying levels of granularity from the stereo
images collected by the AUV (see Section II-E for detail).
In our approach, the visibility coefficients are estimatedby first binning the intensity values in each color channel
with scene distance for all pixels as illustrated in Figure 3.
It can be seen that the range of scene point radiances ingood visibility atmospheric conditions (top left image) are
independent from scene distance and possess a wide rangeof values [0..1] (grey points). However, the deviation ofobserved scene radiances in underwater images (bottom left
image) will decrease with scene distance, depending on
visibility and ambient light, which is subject to wavelengthdependent attenuation through the water [2], [3]. This results
in a narrow field of radiances (colored points), that converge
to the current ambient light intensity.
Using dark and white patch assumptions, thresholdscc(EX%) are applied where EX%
is defined in a way that X%
cof intensity values in color channel c are smaller than EX%.
Black patch assumption: The lowest 1% of irradiances ineach color channel and scene distance bin stem from black
objects
ρc ≈ 0c∀E c ≤ E1%(4)

Fig. 3. The observed pixel brightness variation with distance for underwater
images with and without haze. For illustrative purposes, the underwater
image was obtained from the haze-free image as described in Section III.
The range of scene point radiances in good visibility conditions (top left
image) are independent from scene distance and possess a wide range ofvalues [0..1] (grey points). The deviation of observed scene radiances inunderwater images (bottom left image) will decrease with scene distance,
depending on visibility and ambient light, which is subject to wavelengthdependent attenuation. This results in a narrow field of radiances (coloredpoints), that converge to the current ambient light intensity. Conditional,
non-linear estimation (trusted region) yields good estimates of the ambient
light and attenuation coefficients (colored lines).

Using this assumption directly would lead to halos andproblems with uniformly colored surfaces like it is the casein regional contrast stretching techniques [20]. Image matting
helps prevent halos [6], [8], however matting techniques are
based on structure and intensity information which degrade
the disparity map in the presence of large uniformly shaded
structures or 3D objects that are covering a large scene depthfield. In contrast, we propose to use these scene points in arobust, non-linear estimation framework to achieve a global
solution for all depth bins in one color channel.
White patch assumption: Unless retro-reflective surfaces
are present, which is generally not the case in natural underwater environments, the observed scene point brightness is
upper-bounded by the ambient illumination conditions
ρc ≤ 1,c∀E c ≥ E99%

(5)Inserting all assumptions in (1) yields a conditional, nonlinear estimation problemcc
E c = I∞(z)(1 − e−β d ),c
∀E c ≤ E1%(6)c(z), β c ) and anwith two unknowns per color channel (I∞additional constraint on the ambient light such thatc(z) ≥ E c ,I∞c∀E c ≥ E99%.

(7)

We use a trust region approach for minimizing (6) subject
to the bounds (7) [25]. The optimization is initialized using
the brightest pixel in each color channel for the airlightc(I∞), and the theoretical clear ocean water values for (β c ).
In practice, approximately 12 iterations are required forconvergence. Figure 3 illustrates the estimation process aswell as the validity of both proposed assumptions with theupper line showing the result of the ambient light estimate(7), and the lower curve the attenuation coefficient from (6).
C. Scene Radiance Recovery
Once β c and I∞ (z) are estimated the scene radiancerecovery of all image pixels is performed for each color
channel according to


c
Ec
c
ρ =1+
− 1 eβ d(8)c
I∞ (z)

For this step, a dense disparity map of the image scene isessential to determine the scene distances. The generation ofdense disparity maps is described in Section II-E.
D. Visibility Assessment
To characterize the current underwater visibility condition,
the mean attenuation coefficient (α) that is due to thescattering and absorption of particles is given byP c(β − κc )
α= cP
(9)
c1
where the mean clear ocean water attenuation coefficients
κc for each color channel is determined from the spectral
response sc (λ) of the camera sensor as illustrated in Figure 2:
Z 800nmcsw
κ =sc (λ) ∗ Kw
(λ)dλ
(10)
200nm

This measure relates the current conditions to clear ocean
water conditions (α = 0) which possesses the best theoreticalvisibility. To estimate visibility distance we approximate
human contrast discrimination. Human perception can distinguish two neighbouring scene points as soon as they exceed
a contrast ≥ 5% [26] such that
Emax − Emin
= 0.05(11)
Cvis =
Emax + Emin
Inserting (6)-(7), we derive the visibility distance for AUV
applications in the color channel with best visibility such that

1 − Cvis
1
dvis =
·
log
+
1
(12)
minc (β c )2Cvis
Additionally, we are able to obtain a depth estimate of
the AUV (zest ) by comparing the estimated ambient lighting
conditions with the light attenuation characteristic from clear
ocean water as depicted in Figure 2, such that!
Xc 2zest = arg min(γ c − γth
)
(13)
z
c

c

where γ is the estimated ambient light chromaticity givenbyIc
γc = P 0 c
c I0

(14)

and γth is the theoretical chromaticity-depth-dependency inclear ocean water.
Whilst zest can be very uncertain as it assumes whitesunlight and clear ocean water behaviour, in moderate to highvisibility conditions it yields a good measure for plausibility
checks against other sensors (such as an onboard depth
sensor) in order to achieve higher accuracy and reliability.
E. Image Enhancement
In order to perform efficient enhancement of the visibility degraded underwater images, we propose an approachconsisting of two visibility and enhancement steps utilizing
stereo image pairs as depicted in Figure 4.
The first image enhancement step (step 1) recovers important scene content and image structures in areas with bad

Fig. 4. Flowchart of the proposed image enhancement algorithm consistingof two visibility estimation and two enhancement steps.

visibility. To start the process, a coarse 3D map of the sceneneeds to be created.
We use the LIBELAS1 implementation, proposed in [27],for all stereo retrieval. It is a Bayesian approach to stereo
matching that creates a prior on disparities from robustly
matched support points. The advantage of this implementation is that even in poor visibility where dense stereo
estimation will be challenging, some scene points can berobustly matched. Hence, the prior already implicitly yields
a coarse 3D map of the scene that can be used in the firstvisibility estimation step.
The coarse 3D map is obtained from the robustly matchedscene points via Delaunay triangulation [28]. Using thecoarse 3D map, the visibility coefficient as well as ambient
illumination is estimated using the physical light attenuation
model described in Sections II-A and II-B.
In order to recover the original scene radiances for image
enhancement (Section II-C), a dense and edge preserving
depth map is mandatory. However, due to the degraded input
images, only a coarse disparity mesh is available with many
disparity gaps present which need to be filled. Therefore, we
utilize an image matting approach [29] that refines the coarse
disparity map and extrapolates information to unmatchable
areas, based on the observed intensity values to form a“pseudo 3D map”. It is an edge preserving and halo preventing algorithm but with the disadvantage that the resulting
dense map does not contain the observed disparity values
anymore. However, it is a good starting point for relatively
fast image enhancement and scene structure recovery. In
1 Library for Efficient Large-scale Stereo Matching: http://www.cvlibs.net

conventional disparities

enhanced image

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Fig. 5.
Results on Middlebury Dataset. First column shows degradedunderwater images, rendered with z = 3m, α = 0.2 (art), z = 2m,
α = 0.15 (dolls), z = 5m, α = 0.2 (moebius), z = 1.5m, α = 0.3(aloe). Second column shows their disparity maps. Third column shows the
improved disparities (after step 1). Last column shows enhanced image (step2).

very challenging images, as in some experimental results ofSection IV, where the coarse 3D map covers only a minor
part of the image, we additionally fill disparity gaps byrobustly estimating the predominant 3D plane using Random
Sample Consensus (RANSAC) [30].
In the second enhancement (Fig. 4, step 2), the procedure
is repeated using the enhanced left and right images from(step 1) to create an “improved stereo” map. This map isfurther refined utilizing a discontinuity preserving gap interpolation [31], followed by image inpainting [32] for large
areas with missing 3D information. Using this improved
stereo map, detailed visibility parameters are estimated.
These are then used with the gap interpolated map to recoverscene radiances in the final enhancement (step 2).
III. S IMULATION R ESULTS
Images of complex underwater scenes are difficult to precisely ground-truth. Therefore, the accuracy of our method
was investigated on the Middlebury data set2 , using the image
pairs and ground-truth disparities from aloe, art, dolls andmoebius at full resolution (≈ 1300 × 1100 pixels). Artificial
degradation of the images was performed to approximate theunderwater survey requirements of an AUV in a depth rangeof 0 − 5m and a mean attenuation coefficient α = 0 − 0.3
utilizing the model from Section II-A (without added noise).
Figure 5 shows results after applying the methodology described in Section II-E for different visibility conditions anddepths. Although visibility degradation leads to a significant
information loss in the acquired images they still provide
sufficient information to estimate the visual range and tosome extent considering SNR restore lost image parts. The
original image chromaticity could also be restored. The main
limitations of image enhancement stem from occluded image
regions where no disparities can be matched (see Figure 5,art).
2 http://vision.middlebury.edu/stereo/

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Fig. 6. Improved stereo results for decreasing visibility. Left column showsthe estimated mean attenuation coefficient for decreasing visibility α =0 − 0.3 at depth z = 2.0m (dolls) and z = 1.5m (aloe), respectively.
Center column depicts the fraction of unmatched pixels which is rapidly
increasing in a conventional stereo matching framework. Upstream visibility
estimation enables stereo matching with almost constant quality.

Fig. 7. Image restoration for increasing depth. Left column shows theestimated mean attenuation coefficient for increasing depth z = 0 − 5m
for α = 0.2. Centre column depicts the color difference to the haze-freegroundtruth image. Visual results are shown in the right column.

The improvement for stereo computation was evaluatedfor aloe at z = 1.5m and dolls at z = 2.0m with increasing
mean attenuation coefficient α = 0 − 0.3 (Figure 6). As
shown the visibility conditions could be estimated reliably.
Due to missing black/dark objects in aloe, the black patchassumption was violated which leads the proposed algorithm
to slightly overestimate the current attenuation coefficient.
When performing conventional stereo matching, the fractionof unmatched pixels is rapidly increasing from 0.21 to 0.92
(aloe) and from 0.26 to 0.72 (dolls) with adverse visibility. In
contrast, our method proves to be robust to visibility changes,that is the fraction of unmatched pixels stays below 0.23 forboth images over the whole range of visibility conditions.
The attenuation characteristic of ocean water leads to significant light chromaticity changes for increasing depths. A
popular measure for perceived color differences with respect
to the original image (above the water surface) is the CIE
1976 L*,a*,b* color difference formula [33] that computes
the Euclidean point distance between two color points inthe color-opponent and perceptually uniform CIE L*a*b*(∆EL∗a∗b∗ ). A color difference of ∆EL∗a∗b∗ ≈ 2.3 is theJust noticeable difference (JND) and is the minimum color
deviation which can be distinguished by a human observer.
As depicted in Figure 7, ∆EL∗a∗b∗ between original image

GoPro high-definition
stereo camera

onboard stereo camera

Fig. 8. The Starbug AUV was equipped with an onboard forward looking
stereo camera pair. In addition, a GoPro high-definition stereo camera headwith 32 mm baseline was mounted to the front of the vehicle and recorded
during the experiments.

and underwater image rises with increasing depth z = 1−5mfrom 19.28 to 35.3 (moebius) and from 23.87 to 40.36 (art),
respectively. A first image enhancement leads to an almost
constant color difference ∆EL∗a∗b∗ ≈ 15 (moebius) and∆EL∗a∗b∗ ≈ 18.1 (art). A second restoration step, using
the improved stereo information further reduces the color
deviation to 10.7 and 13.1, respectively.
IV. E XPERIMENTAL R ESULTS
The underwater image enhancement algorithm presented
above was evaluated on an AUV in uncontrolled ocean
conditions. The purpose of these experiments was to evaluate
the robustness and performance of the technique in termsof effective stereo range improvement and ability to correctcolor under different naturally lit operating scenarios.
A. AUV Platform
The Starbug MkIII AUV [34] was used for these experiments (Figure 8). The AUV’s onboard vision system
consists of two stereo camera pairs, one looking forward
and the other looking downwards. For this study an external
high-resolution stereo system was used for image collection. These calibrated GoProTM cameras (baseline 32mm)
collected images at 30 fps with resolution 1280 × 960 andwere time-synchronised to the AUV’s onboard computer.
B. Experiments
A set underwater transects were performed at two sitesin Queensland, Australia. The first was a shallow water,
rather turbid, seagrass survey site off Peel Island (153.377o E,
27.494o S) in Moreton Bay. The second site was in therelatively clearer waters off Heron Island (151.8975o E,
23.4504o S) at the Southern end of the Great Barrier Reef.
1) Peel Island Transects: This area is adjacent to a marine
protected zone and contains a range of seafloor types such as
hard coral, rock, sand and seagrass. As it is a strongly tidal
region, the visibility can vary greatly due to resuspension of
sediments, incoming clear oceanic and turbid flood waters.
Four different length transects were performed with the
AUV and covered water depths from approximately 0.3 to 3
m with the AUV travelling towards and away from the sun.
The terrain was relatively planar sloping gently up towards
and island with protruding coral heads, seagrass (Sargassum)
and small rocks. During the experimental campaign the Secchi depth was measured from 2.5 - 4 m. Weather conditions
were fair with the sun at its maximum height in the sky. In
total, 1494 images were collected from the four transects.

2) Heron Island Transects: This survey site was on asteep sided reef slope and consisted of hard coral going down
to a sand floor with a depth range of 1 - 8 m. Although thesite had considerably greater visibility than in Moreton Bay,strong winds exceeding 25 knots during the experimental
campaign had reduced the visibility to a Secchi depth of 5
- 8 m. In these experiments the horizontally mounted AUV
stereo camera system was used to track another AUV as it
maintained constant altitude above the reef surface. A totalof 870 images were collected during four transects which
travelled up and down the reef slope.
C. Results
A comparison between image enhancement using our approach against established histogram equalisation methods toincrease image contrast are shown for representative images
from Peel and Heron Island in Figure 9. The methods used
for comparison are the standard MATLABTM functions for
spatially-invariant histogram equalisation (histeq), and thespatially-variant adaptive histogram equalisation (adapthisteq) as employed in [8]. It can be seen that our results provide
greater image detail and overall quality compared to thehistogram methods for both turbidity (visibility) scenarios.
Figures 10(a) and 10(b) show the results for the estimated visibility coefficient, the visibility distance and the
diving depth for both the Peel and Heron Island transects
respectively. As seen the proposed approach yields stable
and coherent estimation results for the range of depths andvisibility conditions. However, there can be a slight overestimation of visibility distance when very close to thesurface where the scene depth cannot be reliably estimated(surface occupies a significant portion of the image) and thevisibility coefficient is underestimated.
The estimated depth profiles reflect the actual camera
depth during transects, except for a constant offset of approximately 0.3 m in the Peel Island data. This is likely causedby the large amount of organic material present in the watercolumn which may influence the ambient light chromaticity
and the estimation performance as discussed in Section II-D.
The visibility coefficient estimate shows some outliers inPeel Island Sequence 4 that stem from low textured images
captured in higher diving depths where no disparities could
be reliably matched.
Figures 11(a) and 11(b) show representative images andtheir enhanced image and disparity maps for the Peel Island and Heron Island transects respectively. The enhanced
images allow for automatic visual inspection and obtaining
repeatable observations of benthic habitats. An empirical
study by marine scientists has indicated that they are able
to more reliably classify coral and seagrass species on theenhanced images over the original images, particularly for
the Peel Island transects.
The disparity maps in these experimental conditions show
only moderate improvements with up to 6.3% in unmatchedpixels and range. There are four possible causes of this; (1)imprecise camera calibration (particularly for distant objects
due to the short baseline and lack of a global shutter on the(a) Original image(b) Our approach(a) Peel Island Transects(c) Histogram equalization (d) Adaptive
equalization(e) Original image

histogram(f) Our approach(b) Heron Island Transects
Fig. 10. Estimation results for the visibility coefficient (top trace), thevisibility distance (center trace) and the estimated diving depth (bottom
trace) using four underwater sequences acquired from different diving
transects at (a) the high turbidity water at Peel Island, and (b) the moderateturbidity water at Heron Island.

(g) Histogram equalization (h) Adaptive
equalization

histogram

Fig. 9. Comparison of our approach against histogram equalization methods
for representative images from Peel Island (a)-(d) and Heron Island (e)-(h).

stereo camera rig), (2) aliasing due to small-scale repetitive
structures, (3) the planarity of the scene with the AUV
travelling parallel to the seafloor with only the lower halfcontaining features, and (4) more dominantly due to particles
in the water and inhomogenously scattered light (e.g. fromsun beams in the shallow water).
Table I illustrates the mean and standard deviation
computation times for 640 × 480 pixel images using a single 2.4 GHz CPU running a non-optimized
MATLABTM implementation of the proposed algorithms.
Whilst not quite at framerate the results suggest the proposed
methodology could be implemented onboard slower moving
AUVs for improving navigation and obstacle avoidance.
V. C ONCLUSIONSThis paper has described a novel method that simultaneously performs underwater image quality assessment,
visibility enhancement and improved stereo estimation. We
firstly propose the development of a coarse 3D map from thedegraded input stereo images to allow visibility estimationand image enhancement using a physical model for atmospheric scattering adapted for use underwater in naturally lit

Algorithm
Sparse disparity mesh computation
Visibility estimation (step 1)
Image enhancement L (step 1)
Improved disparity computaton(including image enhancement R)
Visibility estimation (step 2)
Image enhancement (step 2)
TOTAL

Mean (s)0.11710.61390.3149

Std Deviation (s)0.01490.15890.01230.77050.56690.18222.56550.02020.05020.0179TABLE IM EAN AND STANDARD DEVIATION COMPUTATION TIMES FOR EACH
STEP IN THE PROPOSED IMAGE ENHANCEMENT METHOD .

environments. In a second step, the improved stereo map isused for a precise visibility estimation, image enhancement
and 3D scene reconstruction. This approach was evaluatedin simulation and using experimental data collected by
an AUV in reduced visibility, naturally lit coastal waters.
It is shown to improve stereo disparity estimation by up
to 6.3% in the harsh experimental conditions experienced.
This technique has proven robust and its performance haspromise for implementation onboard an AUV and for image
color correction to allow improved species identification bymarine scientists. Future work will focus on investigating
the robustness of stereo matching algorithms to improve thedisparity image particularly in shallow water where surface
waves cause rapidly varying lighting conditions.

(a) Peel Island(b) Heron Island
Fig. 11. Example enhanced underwater images and disparity maps from(a) the high turbidity Peel Island Transects, and (b) the moderate turbidity
Heron Island Transects.

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