Underwater Image Enhancement by the Retinex Inspired Contrast
Enhancer STRESS
Michela Lecca
Fondazione Bruno Kessler, Digital Industry Center, via Sommarive 18, 38123 Trento, Italy
Underwater Images, Image Enhancement, Retinex.
Underwater images are often affected by undesired effects, like noise, color casts and poor detail visibility,
hampering the understanding of the image content. This work proposes to improve the quality of such images
by means of STRESS, a Retinex inspired contrast enhancer originally designed to process general, real-world
pictures. STRESS, which is based on a local color spatial processing inspired to the human vision mechanism,
is here tested and compared with other approaches on the public underwater image dataset UIEB. The exper-
iments show that in general STRESS remarkably increases the quality of the input image, while preserving
its local structure. The images enhanced by STRESS are released for free to enable visual inspection, further
analysis and comparisons.
Underwater imaging is an important support for nav-
igation and for many activities of marine exploration,
such as rescue missions, ecological monitoring, ma-
rine life studying, hand-made structure inspections
(Manzanilla et al., 2019), (Bonin-Font et al., 2008),
(Selby et al., 2011), (Lu et al., 2017), (Pedersen et al.,
2019), (Matos et al., 2016), (McLellan, 2015), (Shee-
han et al., 2020), (Ho et al., 2020), (Yin, 2021). For all
these task, images with good visibility of details and
content are required, but unfortunately underwater
images are usually affected by low-light, non-uniform
illumination, green-bluish color cast, haze and noise.
These issues are mainly due to three physical phe-
nomena of the transmission of the light through the
water medium. First, in the water, the red compo-
nent is absorbed faster than the green and the blue
ones and this generates the green-bluish color that
characterizes the underwater images. Second, there
is the light background scattering, i.e. the diffusion
of the light from sources others than the sample of
interest, including also suspended particles that often
generate noise. Third, there is the light forward scat-
tering, i.e. the deviation of the light from its origi-
nal angle of transmission, that attenuates the light en-
ergy aggravating the visibility of the details and con-
tent of the observed scene. Many image enhancement
techniques have been developed and two recent, in-
teresting reviews are presented in (Li et al., 2019a),
(Wang et al., 2019). In (Li et al., 2019a), these tech-
niques are divided in two groups, named respectively
the IFM-free image enhancement methods and the
IFM-based image restoration methods, where ’IFM’
stands for image formation model. The methods of the
first class enhance underwater images without con-
sidering any physical model of the image formation
in the marine environment. They usually process the
input image by reworking its color distribution and
contrast. Some examples of these methods are the
works in (Hummel, 1977), (Zuiderveld, 1994), (Iqbal
et al., 2010), (Huang et al., 2018), (Vasamsetti et al.,
2017), (Jamadandi and Mudenagudi, 2019), (Fabbri
et al., 2018). On the contrary, the methods of the
second class rely on physical principles of the under-
water imaging process. Basically, they assume that
the image signal is determined by two energy compo-
nents relative to the light directly transmitted from the
observed scene to the camera and to the light back-
ground scattering. Some examples of these methods
are the works in (Chao and Wang, 2010), (Serikawa
and Lu, 2014), (Zhao et al., 2015), (Li et al., 2016a),
(Li et al., 2016b), (Wang et al., 2017), (Shi et al.,
2018) (Barbosa et al., 2018). Anyway, despite the
many efforts on underwater image enhancement, this
task is not yet solved and remains still today an active
research field.
This work investigates the usage of the Retinex-
inspired, real-world image enhancer STRESS (Kol˚as
Lecca, M.
Underwater Image Enhancement by the Retinex Inspired Contrast Enhancer STRESS.
DOI: 10.5220/0010895700003124
In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages
ISBN: 978-989-758-555-5; ISSN: 2184-4321
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
et al., 2011) as an IFM-free tool for improving the
quality of underwater pictures. STRESS basically
implements a local equalization of the red, green
and blue channels. For each channel and for each
pixel x, STRESS randomly samples a set of pixels
around x and stretches the channel intensity distri-
bution around x between two values selected from
the sampled pixels. These values vary from pixel to
pixel and form two surfaces bounding the channel and
called envelopes. Stretching the channel between its
envelopes increases the contrast granting a better vis-
ibility of the image details and content. The name
STRESS is an acronym of the expression ’Spatio-
temporal Retinex-like Envelope with Stochastic Sam-
pling’ which refers to the main characteristics of this
algorithm and with the fact that it was originally ap-
plied for time sequence enhancement. Developed as
a contrast enhancer for general, real-world image en-
hancer, STRESS is here tested as an IFM-free under-
water image enhancer on the public, popular dataset
UIEB (Li et al., 2019b), which contains 890 marine
images with corresponding high quality references.
The experiments show that STRESS remarkably im-
proves the quality of the input image, increasing its
contrast and dynamic range while preserving as much
as possible its original structure. Moreover,compared
with other algorithms of the same class, STRESS ex-
hibits similar or better performance.
The paper is organized as follows: Section 2 de-
scribes STRESS; Section 3 explains how STRESS is
evaluated in the framework of underwater image en-
hancement; Section 4 reports the results, while Sec-
tion 5 draws some conclusions and future work.
STRESS is a spatial color algorithm inspired by
Retinex theory (Land et al., 1971), (Land, 1964).
Figure 1: An example of simultaneous contrast: a same tri-
angle is displayed against two different backgrounds, but
the triangle seems to be brighter on left than on right. This
is because the color of a point as perceived by humans is
influenced by the surrounding colors.
Retinex was developed by Land and McCann at
the end of 1970s as a model to predict how humans
see colors. Based on a set of empirical evidences,
Retinex highlighted that human vision system and
cameras work differently. In fact, when looking at a
point of a scene, humans and cameras may report very
different colors, as the simultaneous contrast phe-
nomenon proves (see Figure 1). This is because the
color of a point as perceived by the human vision sys-
tem depends not only on the photometric properties
of that point, but also on the surrounding colors. This
fact suggested to Land and McCann to reproduce the
human color sensation from a digital image by an al-
gorithm, namely the Retinex algorithm, that reworks
the colors of each pixel based on the spatial distribu-
tion of the colors of its neighbors. Local color pro-
cessing is performed independently on the red, green
and blue channels of the input image. This is in line
with the empirical evidence that humans acquire and
process separately the short, medium and long wave-
lengths of the light. The Retinex algorithm has been
widely studied because its local spatial color analysis
can be used to improve the visibility of image details
and content as well as to attenuate or even remove
possible color casts due to the light. Many variants of
this processing mechanism have been developed and
others are still under development, proposing differ-
ent approaches to combine local spatial and color in-
formation with the final purpose to enhance the input
STRESS arises from the Milano Retinex family
(Rizzi and Bonanomi, 2017), (Lecca, 2020), a set
of algorithms grounded on Retinex principles and
mainly used for image enhancement. These algo-
rithms differ to each other in the definition of the spa-
tial support where to compute the local spatial color
distribution, in the way the spatial and channel in-
tensity information are combined for improving the
input image, and consequently in the final enhance-
ment level. Precisely, STRESS is a variant of the Mi-
lano Retinex algorithm Random Spray Retinex (RSR)
(Provenzi et al., 2007). RSR takes as input a color
image and works separately on its color channels. For
each channel I of the input image and for each pixel
x of I with channel intensity I(x), RSR computes N
random sprays, i.e. N sets S
(x), . . . , S
(x) of k pixels
randomly selected with radial distribution around x.
RSR extracts from each S
(x) the maximum intensity
value M
and maps the intensity I(x) onto a new value
L(x), called the lightness of I at x and defined as:
L(x) =
where I is of course processed in order to prevent di-
vision by zero. The number k models the locality
of the color processing, i.e. the higher k the higher
the points sampled around x and closer to x. Con-
sidering more than one spray (i.e. N > 1) enables
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
to mitigate possible chromatic noise due to the ran-
dom sampling. The final enhanced image is obtained
by rescaling the values of each channel lightness over
{0, . . . ,255} and packing the rescaled lightnesses as
a RGB image. Rescaling the value I(x) by the max-
imum values over each sprays enables stretching the
local distribution of the channel intensity, increasing
the channel brightness and contrast, and, according
to (Lecca, 2014), lowering eventual chromatic domi-
nants of the light.
The algorithm STRESS originated from RSR
to further improve the image contrast. As RSR,
STRESS processes the channels of the input image
separately and for each channel I it implements the
pixel-wise random sampling scheme of RSR. Differ-
ently from RSR, STRESS extracts both the minimum
and the maximum intensity of each spray sampled on
I and uses these values to define two surfaces E
and E
, called envelopes and bounding the channel.
The intensity values of the channels are then stretched
pixel-wise between the corresponding values in the
lower and upper envelopes. This operation locally
stretches the local distribution of I, thus increases the
contrast of the image improving the visibility of the
image details.
Mathematically, given a pixel x of I and the N sprays
(x)(i = 1, .., N), let E
(x) and E
(x) be the min-
imum and maximum intensities over the spray S
and let R
(x) be their distance, i.e.:
(x) = min{I(y) : y S
(x)}, (2)
(x) = max{I(y) : y S
(x)}, (3)
(x) = E
(x) E
(x). (4)
STRESS maps the value I(x) onto a new value v
basically obtained by stretching I(x) between E
and E
(x) when these are different, while set to 0.5
(i.e. in the middle of the possible intensity range) oth-
(x) =
if R
(x) = 0
As in RSR, more sprays are considered to reduce un-
desired effects of the random sampling. Specifically,
the values R
(x) and v
(x) are averaged over N:
R(x) =
(x), v(x) =
(x). (6)
The envelopes containing the channel I are computed
as follows:
(x) = I(x) R(x)v(x), (7)
(x) = E
(x) + R(x), (8)
and the value of I(x) is mapped on the value
STRESS(x) defined as:
if E
(x) = E
Figure 2 shows an image and its enhancement by
STRESS, along with an example of random spray
computed around the barycenter of the red channel
pixels and the lower and upper envelopes of the red
The evaluation of the performance of STRESS em-
ployed as an IFM-free underwater image enhancer is
carried out on the public dataset UIEB
. This dataset
contains 890 real-world images, some of them cap-
tured with the camera oriented from the seabed to the
water surface, others acquired in the opposite direc-
tion. The UIEB scenes have been taken under differ-
ent illuminations, including both natural and artificial
lights as well as a combination of them. The images
present different issues for enhancement, like e.g. low
light, veil, noise, green-bluish color cast. For each
image J, the dataset contains also the reference of J,
i.e. a ’ground-truth’ version of J having good qual-
ity. This reference has been selected by 50 volunteers
from a set of versions of J enhanced by different al-
gorithms. Some examples of images from UIEB are
shown in Figure 3.
Assessing a quality of an image is a hard task, not
yet fully agreed (Pedersen and Hardeberg, 2012) and
in general a single measure does not suffice to capture
all the features concurring to the image quality (Bar-
ricelli et al., 2020). Therefore, here the performance
of STRESS has been evaluated by analyzing both no-
reference and full-reference measures.
The no-reference measures considered here are:
1. the mean brightness of J (B): B is the mean value
of the intensity of the mono-chromatic image BJ
obtained by averaging pixel by pixel the three
color channels of J;
2. the mean multi-resolution contrast (C) (Rizzi
et al., 2004): C is the mean value of pixel-wise
contrasts c computed on multiple scaled versions
of BJ; in particular, the contrast c(x) of a pixel x
of any rescaled version of BJ is defined as the av-
erage of the absolute differences between the in-
tensity at x and the intensities of the pixels located
in a 3×3 window centered at x;
Underwater Image Enhancement by the Retinex Inspired Contrast Enhancer STRESS
Figure 2: On top: an image, its enhancement by STRESS and its red channel, where the red circles belong to a random spray
centered at the barycenter of the channel. On bottom: the lower and upper envelopes of the red channel computed using 10
sprays each with 250 pixels.
3. the histogram flatness (f): f measures the entropy
of the intensity distribution of BJ and is defined
as the L
distance between the probability density
function of the intensity of BJ and the uniform
probability density function;
4. the human vision system vision inspired metric
UIQM (Panetta et al., 2015): this measure ac-
counts for three features highly relevant to the hu-
man vision system to assess the readability of an
underwater image, i.e. the colorfulness, the sharp-
ness and the contrast of the image. UIQM has
been designed to correlate with the human percep-
tion of the quality of an underwater image;
5. the underwater color image quality evaluation
metric UCIQE (Yang and Sowmya, 2015): this
measure, related to human perception, accounts
for the chroma, the saturation and the contrast of
the input image.
The values of B, C and f computed on the input
image are compared with those computed on the out-
put image. In general, an enhancer is expected to in-
crease the brightness and the contrast, while decreas-
ing the histogram flatness. The values of UIQM and
UCIQE are expected to be proportional with the im-
age quality, i.e. they should be high for images with
good quality and low otherwise.
The full-reference metrics considered here are:
1. the mean square root (MSE): this is the L
tance between the reference image and the pro-
cessed one and thus captures color distortions pos-
sibly introduced by enhancing;
2. the peak-signal-to-noise-ratio (PSNR): PSNR
also measures differences between the reference
and the processed image but in a logarithmic
3. the structural similarity (SSIM) (Wang et al.,
2004): this measure captures local distortions of
the image structure accounting for the covariance
of the image channel intensities in image sub-
4. the percentage of enhanced images with red-
distored pixels (SP) and the percentage of red-
distorted pixels in these images (MSP): in general,
the red component of underwater images has low
values and variance with respect to the other com-
ponents. Stretching the variability range of the red
channel in order to augment the information con-
tained in may generate saturation. This means that
the red values of some pixels are moved towards
their maximum value, i.e. 255, introducing arti-
facts and making the image poorly natural. Such
color distortions are here evaluated by detecting
the pixels in the enhanced image such that (a) the
red (R), green (G) and blue (B) components are
close to red, i.e.:
R > 200 & R > 10G & R > 10B, (10)
and (b) the inequalities in (10) do not hold for
the corresponding pixels in the reference image.
Mathematically, let p be a pixel in the enhanced
image satisfying the inequalities of formula (10)
and let p
be its corresponding pixel in the refer-
ence. If the color of p
does not match the condi-
tions in (10), then p is considered as red-distorted.
SP measures the percentage of enhanced images
containing red-distorted pixels, while MSP is the
percentage of red-distorted pixels in these images.
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
These measures compare the reference to the en-
hanced image. The lower the MSE, the higher the
PSNR and the SSIM, the closer the enhancedimage to
the reference is. The higher the value of SP, the higher
the quantity of red-distorted pixels is and the worse
the quality of the enhanced image is. The higher MSP,
the worse the enhancer performance on the test set is.
The images of UIEB have been processed by
STRESS, where the number of sprays per pixel N is
set to 10 and each spray contains k = 250 pixels. The
images enhanced by STRESS are available for free
by citing this paper
to enable visual inspection, fur-
ther analysis and comparison. By the way, the perfor-
mance of STRESS is compared with those obtained
by six IFM-free underwater image enhancers, i.e.:
the histogram equalization (HE) (Hummel, 1977),
the contrast-limited adaptive histogram equalization
(CLAHE) (Zuiderveld, 1994), the unsupervised von
Kries based color correction via histogram stretch-
ing (UCM) (Iqbal et al., 2010), the global histogram
stretching for underwater images working in the CIE
Lab color space (RGHS) (Huang et al., 2018), the
gamma correction algorithm (GC), the optimized ver-
sion of RSR called Light-RSR (Bani´c and Lonˇcari´c,
2013) and the algorithm SuPeR (Lecca and Mes-
selodi, 2019) fromthe Milano Retinexfamily. HE and
CLAHE are popular algorithms processing the color
histogram of the input image respectively globally
and locally in order to maximize the image bright-
ness and contrast. UCM corrects the image color and
contrast by diminishing the blue intensity, increasing
the red one, and reworking intensity and saturation
on the HSI color space. RGHS increases the contrast
by equalizing the green and blue components, redis-
tributing the channel histograms and applying a bilat-
eral filter to remove noise. GC increases the bright-
ness and contrast of low light images by raising the
channel components of the image by a factor γ, usu-
ally set to 2.2. Light-RSR is an optimized version of
RSR, which uses a single spray per pixel (N = 1) and
attenuates the chromatic noise by box filters. By this
way, Light-RSR enhances the image similarly to RSR
but with a much lower computation time. SuPeR is
another, fast Milano Retinex algorithm, that improves
the image by rescaling the channel intensities of any
pixel x by a factor inversely proportional to the aver-
age of a set of intensities selected from tiles defined
over the image and weighted by their distance from x.
All these algorithms have been run on UIEB by using
their implementation available on the net. For Light-
RSR, k is set to 250, for SuPeR the number of tiles per
image is fixed to 25, while for the other algorithms the
default parameters provided in their codes have been
used. Figure 3 shows some examples of enhancement
of UIEB images by these algorithms.
Table 1 reports the performance of STRESS in
comparison with the other enhancers. The values of
the different measures described in the previous Sec-
tion are averaged on the number of images of UIEB
and computed also for the original and reference im-
ages where this makes sense. On average, STRESS
remarkably increases the image contrast, making it
very close to that of the UIEB references, while the
brightness remains close to that of the input images.
STRESS also decreases the value of f making it close
to that of the references, while returns the lowest
(highest, resp.) value of MSE (PSNR, resp.) and a
quite high value of SSIM, meaning that in the col-
ors and the local structure of the enhanced images are
close to those of the corresponding references. More-
over, STRESS returns quite high values of UIQM and
UCIQE, close to those computed on the references.
This indicates that STRESS provides in general natu-
ral images. Nevertheless, as already observed in (Li
et al., 2019b), in some cases, the values of UIQM and
UCIQE does not reproduce correctly the human per-
ception about the naturalness of the underwater im-
ages. In fact, the algorithm HE often over-enhances
the image, returning very bright pictures with many
red-distorted pixels (see SP), but reports the highest
values of UIQM and UCIQE, exceeding also those
measured on the references. This unexpected be-
haviour is ascribed in (Li et al., 2019b) to the fact that
the human perception in underwater environments is
not yet fully studied and this adversely affects the
modeling of metrics for the assessment of underwa-
ter images. A qualitative visual inspection of the
results shows that HE and UCM are the worse al-
gorithms in terms of red-distorted pixels and noise,
while STRESS reports on average the 0.0158 % red-
distorted pixels in the 5.73% of the images. The high-
est values of MSP are measured on images with very
low-light regions or with very narrow red distribution
(see for instance the last example in Figure 3).
On average, STRESS performs similarly to UCM
and RGHS in terms of brightness, to CLAHE, RGHS
and UCM in terms of contrast and flatness, to CLAHE
and UCM in terms of UIQM, to RGHS and UCM in
terms of UCIQE and to CLAHE and Light-RSR in
terms of MSP. These observations, based on the quan-
titative analysis of the values in Table 1, are inline
Underwater Image Enhancement by the Retinex Inspired Contrast Enhancer STRESS
Figure 3: Examples of underwater image enhancement by different algorithms.
with the visual inspection of the images enhanced by
these algorithms (see Figure 3 for an example).
This work presents the use of STRESS as underwater
image enhancer, tests and compares it on the public
data UIEB, making the results freely available to en-
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
Table 1: Image quality measures for different image enhancers applied on UIEB. INPUT and REFERENCE report respectively
the values of B, C, f, UIQM and ICQE computed on the images and references of UIEB.
] [×10
REFERENCE 111.572 19.896 2.428 - - - 0.960 5.339 -
INPUT 106.405 11.869 3.936 1.768 17.355 0.773 0.542 4.189 -
HE 126.280 23.618 1.188 1.827 16.627 0.775 1.266 7.075 (28.764, 0.3535)
CLAHE 112.466 20.276 2.777 1.175 18.551 0.849 0.940 4.862 (1.124, 0.0122)
GC 136.915 11.756 3.982 2.423 15.519 0.753 0.702 3.687 (0, 0)
RGHS 104.232 17.476 2.617 1.152 19.222 0.825 0.768 5.775 (0.449, 0.0334)
UCM 104.406 18.366 2.708 1.662 17.464 0.799 0.934 6.044 (17.30 0.3429)
LIGHT-RSR 132.524 14.869 3.321 1.997 17.339 0.808 0.816 4.596 (1.124, 0.0141)
SuPeR 133.517 13.678 3.565 2.159 16.804 0.783 0.797 4.219 (0.225, 0.0006)
STRESS 105.747 18.449 2.559 0.792 21.300 0.804 0.942 5.816 (5.730, 0.0158)
able visual inspection and further analysis and com-
parison. The experiments show that STRESS, origi-
nally devised as a real-world image enhancer and in-
spired by the local spatial color processing performed
by the human vision system, effectively improves the
contrast and the dynamic range of underwater images,
preserving also their local structure and fidelity to ref-
erences. The quality of the enhanced images is in gen-
eral good and in line to or even better than other al-
gorithms not based on underwater imaging physical
models but used for this task. Nevertheless, the ex-
periments also highlight one main issue that should
be addressed in future work to guarantee better per-
formance, i.e. the generation of red-distorted pix-
els that affect the quality of the enhanced images.
Anyway, this problems is critical also for other algo-
rithms, as shown by the results obtained by HE and
UCM. In this respect, integrating in STRESS some
physical information may help to overcome this prob-
lem, although this has been observed also for some
IFM-based approaches, e.g. (Li et al., 2016b). In
this framework, future work will theoretically and
practically analyze and compare IFM-based methods
against STRESS.
Bani´c, N. and Lonˇcari´c, S. (2013). Light random sprays
retinex: exploiting the noisy illumination estimation.
IEEE Signal Processing Letters, 20(12):1240–1243.
Barbosa, W. V., Amaral, H. G., Rocha, T. L., and Nasci-
mento, E. R. (2018). Visual-quality-driven learning
for underwater vision enhancement. In 2018 25th
IEEE Int. Conference on Image Processing (ICIP),
Athens, Greece, pages 3933–3937. IEEE.
Barricelli, B. R., Casiraghi, E., Lecca, M., Plutino, A., and
Rizzi, A. (2020). A cockpit of multiple measures for
assessing film restoration quality. Pattern Recognition
Letters, 131:178–184.
Bonin-Font, F., Ortiz, A., and Oliver, G. (2008). Visual
navigation for mobile robots: A survey. Journal of
intelligent and robotic systems, 53(3):263–296.
Chao, L. and Wang, M. (2010). Removal of water scatter-
ing. In 2010 2nd int. conference on computer engi-
neering and technology, Chengdu, China, volume 2,
pages V2–35. IEEE.
Fabbri, C., Islam, M. J., and Sattar, J. (2018). Enhancing
underwater imagery using generative adversarial net-
works. In 2018 IEEE Int. Conference on Robotics and
Automation (ICRA), Brisbane, Australia, pages 7159–
7165. IEEE.
Ho, M., El-Borgi, S., Patil, D., and Song, G. (2020). Inspec-
tion and monitoring systems subsea pipelines: A re-
view paper. Structural Health Monitoring, 19(2):606–
Huang, D., Wang, Y., Song, W., Sequeira, J., and Mavro-
matis, S. (2018). Shallow-water image enhancement
using relative global histogram stretching based on
adaptive parameter acquisition. In Int. conference on
multimedia modeling, Bangkik, Turkey, pages 453–
465. Springer.
Hummel, R. (1977). Image enhancement by histogram
transformation. Computer Graphics and Image Pro-
cessing, 6(2):184–195.
Iqbal, K., Odetayo, M., James, A., Salam, R. A., and Talib,
A. Z. H. (2010). Enhancing the low quality images
using unsupervised colour correction method. In 2010
IEEE Int. Conference on Systems, Man and Cybernet-
ics, Instanbul, Turkey, pages 1703–1709. IEEE.
Jamadandi, A. and Mudenagudi, U. (2019). Exemplar-
based underwater image enhancement augmented
by wavelet corrected transforms. In Proc. of the
IEEE/CVF Conference on Computer Vision and Pat-
tern Recognition Workshops, Long Beach, CA, USA,
pages 11–17.
Kol˚as, Ø., Farup, I., and Rizzi, A. (2011). Spatio-temporal
Retinex-inspired envelope with stochastic sampling:
A framework for spatial color algorithms. Journal of
Imaging Science and Technology, 55(4):1–10.
Land, E. (1964). The Retinex. American Scientistg,
Land, E. H., John, and McCann, J. (1971). Lightness and
Retinex theory. Journal of the Optical Society of
America, 1:1–11.
Underwater Image Enhancement by the Retinex Inspired Contrast Enhancer STRESS
Lecca, M. (2014). On the von Kries Model: Estimation,
Dependence on Light and Device, and Applications,
pages 95–135. Springer Netherlands, Dordrecht.
Lecca, M. (2020). Generalized equation for real-world im-
age enhancement by Milano Retinex family. J. Opt.
Soc. Am. A, 37(5):849–858.
Lecca, M. and Messelodi, S. (2019). Super: Milano retinex
implementation exploiting a regular image grid. JOSA
A, 36(8):1423–1432.
Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., and
Tao, D. (2019a). An underwater image enhancement
benchmark dataset and beyond. IEEE Transactions on
Image Processing, 29:4376–4389.
Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., and
Tao, D. (2019b). An underwater image enhancement
benchmark dataset and beyond. IEEE Transactions on
Image Processing, 29:4376–4389.
Li, C., Quo, J., Pang, Y., Chen, S., and Wang, J. (2016a).
Single underwater image restoration by blue-green
channels dehazing and red channel correction. In
IEEE Int. Conference on Acoustics, Speech and Signal
Processing (ICASSP), Shanghai, China, pages 1731–
Li, C.-Y., Guo, J.-C., Cong, R.-M., Pang, Y.-W., and Wang,
B. (2016b). Underwater image enhancement by de-
hazing with minimum information loss and histogram
distribution prior. IEEE Transactions on Image Pro-
cessing, 25(12):5664–5677.
Lu, H., Li, Y., and Serikawa, S. (2017). Computer vision for
ocean observing. In Artificial Intelligence and Com-
puter Vision, pages 1–16. Springer.
Manzanilla, A., Reyes, S., Garcia, M., Mercado, D., and
Lozano, R. (2019). Autonomous navigation for un-
manned underwater vehicles: Real-time experiments
using computer vision. IEEE Robotics and Automa-
tion Letters, 4(2):1351–1356.
Matos, A., Martins, A., Dias, A., Ferreira, B., Almeida,
J. M., Ferreira, H., Amaral, G., Figueiredo, A.,
Almeida, R., and Silva, F. (2016). Multiple robot op-
erations for maritime search and rescue in eurathlon
2015 competition. In OCEANS 2016-Shanghai, pages
1–7. IEEE.
McLellan, B. C. (2015). Sustainability assessment of deep
ocean resources. Procedia Environmental Sciences,
28:502–508. The 5th Sustainable Future for Human
Security (SustaiN 2014).
Panetta, K., Gao, C., and Agaian, S. (2015). Human-visual-
system-inspired underwater image quality measures.
IEEE Journal of Oceanic Engineering, 41(3):541–
Pedersen, M., Bruslund Haurum, J., Gade, R., and Moes-
lund, T. B. (2019). Detection of marine animals in
a new underwater dataset with varying visibility. In
Proc. of the IEEE/CVF Conf. on Computer Vision
and Pattern Recognition Workshops, Long Beach, CA,
USA, pages 18–26.
Pedersen, M. and Hardeberg, J. Y. (2012). Full-reference
image quality metrics: Classification and evaluation.
Foundations and Trends in Computer Graphics and
Vision, 7(1):1–80.
Provenzi, E., Fierro, M., Rizzi, A., De Carli, L., Gadia, D.,
and Marini, D. (2007). Random Spray Retinex: A
new Retinex implementation to investigate the local
properties of the model. Trans. Img. Proc., 16(1):162–
Rizzi, A., Algeri, T., Medeghini, G., and Marini, D. (2004).
A proposal for contrast measure in digital images. In
Conference on colour in graphics, imaging, and vi-
sion, Aachen, Germany, volume 2004, pages 187–
192. Society for Imaging Science and Technology.
Rizzi, A. and Bonanomi, C. (2017). Milano Retinex fam-
ily. Journal of Electronic Imaging, 26(3):031207–
Selby, W., Corke, P., and Rus, D. (2011). Autonomous
aerial navigation and tracking of marine animals. In
Proc. of the Australian Conference on Robotics and
Automation (ACRA), Melbourne, Australia.
Serikawa, S. and Lu, H. (2014). Underwater image dehaz-
ing using joint trilateral filter. Computers & Electrical
Engineering, 40(1):41–50.
Sheehan, E. V., Bridger, D., Nancollas, S. J., and Pittman,
S. J. (2020). Pelagicam: a novel underwater imag-
ing system with computer vision for semi-automated
monitoring of mobile marine fauna at offshore struc-
tures. Environmental monitoring and assessment,
Shi, X., Ueno, K., Oshikiri, T., Sun, Q., Sasaki, K., and
Misawa, H. (2018). Enhanced water splitting under
modal strong coupling conditions. Nature nanotech-
nology, 13(10):953–958.
Vasamsetti, S., Mittal, N., Neelapu, B. C., and Sardana,
H. K. (2017). Wavelet based perspective on vari-
ational enhancement technique for underwater im-
agery. Ocean Engineering, 141:88–100.
Wang, N., Zheng, H., and Zheng, B. (2017). Underwater
image restoration via maximum attenuation identifi-
cation. IEEE Access, 5:18941–18952.
Wang, Y., Song, W., Fortino, G., Qi, L.-Z., Zhang, W.,
and Liotta, A. (2019). An experimental-based review
of image enhancement and image restoration meth-
ods for underwater imaging. IEEE Access, 7:140233–
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P.
(2004). Image quality assessment: from error visi-
bility to structural similarity. IEEE transactions on
image processing, 13(4):600–612.
Yang, M. and Sowmya, A. (2015). An underwater color
image quality evaluation metric. IEEE Transactions
on Image Processing, 24(12):6062–6071.
Yin, F. (2021). Inspection robot for submarine pipeline
based on machine vision. Journal of Physics: Con-
ference Series, 1952:022034.
Zhao, X., Jin, T., and Qu, S. (2015). Deriving inherent op-
tical properties from background color and underwa-
ter image enhancement. Ocean Engineering, 94:163–
Zuiderveld, K. (1994). Contrast limited adaptive histogram
equalization. Graphics gems, pages 474–485.
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications