Contemporary Engineering Sciences, Vol. 10, 2017, no. 16, 751 - 771

HIKARI Ltd, www.m-hikari.com

https://doi.org/10.12988/ces.2017.7880

Analyzing Pre-Processing Filters Sequences for

Underwater-Image Enhancement

A. Ceballos, I. Diaz Bolaño and G. Sanchez-Torres

Engineer Faculty, Universidad del Magdalena

Carrera 32 No. 22-08, Santa Marta, Colombia

Copyright © 2017 A. Ceballos, I. Diaz Bolaño and G. Sanchez-Torres. This article is distributed

under the Creative Commons Attribution License, which permits unrestricted use, distribution, and

reproduction in any medium, provided the original work is properly cited.

Abstract

Enhancement of the quality of a digital image is desirable in several scenarios such

as underwater-image analysis, where improving visibility is necessary to reduce

alterations caused by unbalanced lighting and the presence of sediments, among

others. Many algorithms have been proposed oriented towards treatment of specific

factors, such as contrast, color fidelity, noise, and lighting. This work explores the

literature techniques and establishes a filter sequence for enhancing underwater

images based on a pre-established quality metric. The resulting sequence begins by

balancing the lighting using homomorphic filtering, improve the contrast by the

Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, and

finally noise reduction and edge enhancement are done by using bilateral filtering.

The results of the implementation suggest a qualitative improvement, in contrast,

color, and sharpness of borders.

Keywords: Image Processing, Underwater Image Enhancement, Algorithms,

Contrast, Fish

1 Introduction

Digital image processing is a branch of signal processing whose objective is to

obtain either a processed image or a set of characteristics from a digital image [1].

A digital image is defined as a bi-dimensional function 𝑓(𝑥, 𝑦) that specifies one or

more levels of intensity depending on the color model for every coordinate (𝑥, 𝑦) or pixel [2]. The most common procedures used to improve visibility are contrast

enhancement, noise reduction, and degradation and reconstruction models.

752 A. Ceballos et al.

Due to their attributes, such as reduced visibility and prevalence of specific

color tones, underwater environments add several additional difficulties because of

the density of the medium and refraction. This gradually diminishes the intensity of

colors that differ from blue as a function of depth (see Fig. 1). This results in

reduced contrast, weak colors, and a visibility range between 5 and 20 m [3] or even

less if there are disturbances in the water [4]. Underwater image (UI) treatment must

consider physical phenomena originating from the medium and the effects resulting

from the presence of organic and inorganic matter.

The UI treatment techniques have become a tool for pre-processing used by

computer vision systems that require the identification of objects in underwater

environments [5]. These techniques can be classified into two categories:

restoration and enhancement techniques [3]. The first defines a model that simulates

the degradation of the scene to reconstruct it.

Enhancement methods improve visibility through manipulation of the components

of the scene. The latter are faster and simpler than image restoration, as they do not

involve physical models of the formation of the image.

Fig. 1: Underwater penetration of the different light waves [6].

UI enhancement is defined as a process that receives a low-quality image as input

and generates as output an image with some increased features [7]. These

techniques improve images through the enhancement of their characteristics. The

contrast and noise characteristics are the most frequently mentioned in the literature

[1], [3].

Measuring the effectiveness of these techniques is usually done through the

Medium Square Error (MSE) [7], [8] and Signal-to-Noise Ratio (SNR) [8]. Both

metrics depend on having both the degraded image and an under graded copy to

compare. If such images are not available, quality can instead be measured through

edge detection algorithms and histogram analysis [9].

This work aims to provide a methodology for UI enhancement through applying

existing techniques oriented towards the treatment of their main deficiencies:

lightning enhancement, contrast enhancement, and noise reduction.

Analyzing pre-processing filters sequences 753

2 Previous Work

A proposed taxonomy of UI enhancement methods can be classified considers five

categories [3]:

a. Histogram and contrast stretching and manipulation.

b. The retinex-based model.

c. Techniques based on filters.

d. Polarization or stereo photography.

e. Mixed techniques.

2.1 Histogram and Contrast Stretching and Manipulation.

These are the most common techniques for the enhancement of UIs. They

consist in the extraction of the intensity histograms in order to normalize them either

through equalization or stretching establishing a desired range of values to which

the pixels in the image are restricted. Methods such as contrast stretching, histogram

equalization, and Contrast Limited Adaptive Histogram Equalization (CLAHE) are

some examples [1], [7], [10]–[12], with CLAHE being one of the most recurrent

with versions based on the RGB (Red, Green, Blue) and HSV (Hue, Saturation,

Value) / HSI (Hue, Saturation, Intensity) color models [8]. The stretching of

intensity values of an image is defined as [13]:

𝐼𝑖,𝑗 =

{

𝐼𝑖,𝑗 − 𝑚𝑖𝑛𝐼

𝑚𝑎𝑥𝐼 − 𝑚𝑖𝑛𝐼𝑖𝑓 0 < 𝐼𝑖,𝑗 < 1

0 𝑖𝑓 0 > 𝐼𝑖,𝑗1 𝑖𝑓 1 < 𝐼𝑖,𝑗

(1)

In [14] proposed a method based on stretching several attributes of the

image. It begins with the use of a stretching algorithm to normalize the contrast of

images in the RGB color model. Afterward, the image is transformed into the HSI

model and stretching is done on the saturation and intensity layers to increase the

color quality and to solve lighting problems. The enhanced image is then obtained

by transforming it back to the RGB color model. In [15] observed differences in

effectiveness between the use of Adobe Photoshop’s color correction technique

with manual and automatic adjustment mode. They worked with under- and

overexposed UIs with depths of between 1 and 5 m.

In [16] proposed the color correction based on machine learning by Markov random

fields. The color of input images is corrected with the value that best describes its

surroundings.

In [17] used contrast stretching. Local contrast is treated using CLAHE. The

resulting images have contrast enhancement at both local and global levels and are

successfully used to perform image matching. Histogram manipulation is an

effective way of improving UIs, but it is often necessary to couple it with other

techniques to remove the enhance noise.

754 A. Ceballos et al.

2.2 The Retinex-based Model

The retinex theory uses the capacity of the human brain to recognize two

instances of the same color under different lighting conditions as being the same

color; that is, it searches for the constancy or invariance of color [18].

Computationally speaking, its goal is to eliminate the impact of the lighting

component and to obtain the real component from the image resulting in improved

images when there is little contrast due to inhomogeneous lighting [3].

Chambah et al. [19] applied constancy color theory for the real-time recognition

of fish. The methodology implements an Adaptative Contrast Equalization (ACE)

method that combines the mechanisms of the gray world and white patch methods

with histogram equalization to obtain an adequate constancy for both lighting and

contrast, avoiding the creation of an inverse color emission over the image and

facilitating subsequent segmentation and feature extraction.

2.3 Techniques Based on filters

Filter-based techniques use highly efficient filtering methods to reduce

noise to improve quality in UIs [3]. This noise is often a result of the acquisition

process of the image, resulting in pixel values that do not represent the real intensity

of the scene [10]. Within this category, methods such as homomorphic filtering,

anisotropic filtering, bilateral filtering, linear filtering, median filtering, and wavelet

noise reduction, among others, can be found [10]–[12], [20].

Arnold-Bos et al. [21] describe a methodology for the enhancement of UI

through the use of filters. The first step is contrast equalization using a low-pass

filter and histogram thresholding to treat lighting inequalities. This step increases

noise, so an adaptive smoothing filter is applied to reduce noise in the borders of

objects. Two kinds of adaptive filtering are tested: anisotropic filtering, which

requires manually setting diffusion constants, and Kovesi’s wavelet filtering, which

is applied automatically.

Prabhakar and Kumar [22] presented an algorithm based on several image-

filtering methods. The first filter is the homomorphic filter, which corrects non-

uniform lighting, improves contrast, and then performs wavelet noise reduction.

The last filtering step is bilateral filtering, which smooths the image while

preserving borders through a non-linear combination of image values close to each

other. Finally, contrast stretching is applied to improve contrast and color correction

is used to balance colors. The algorithm was tested with images where objects were

at distances of between 1 and 2 m from the camera. Applying filters is effective

when needed to treat specific problems in images, but when handling UIs, several

attributes need to be corrected at the same time, resulting in the need to apply more

than one method and, consequently, a higher processing cost.

Analyzing pre-processing filters sequences 755

2.4 Polarization or Stereo Photography

Polarization techniques consist in using several images of the same scene

taken rapidly with a variable grade of polarization [7]. These techniques are hard to

apply in subaquatic environments where the speed of variation of the environment

exceeds the acquisition speed. Techniques based on stereo photography avoid this

problem by requiring two images of the same object taken from distinct angles.

Although they can be used to enhance some of the image attributes directly, they

are mostly applied in image restoration.

2.5 Mixed Techniques

Mixed techniques are remarkable for their often-consecutive application of more

than one technique in the same image enhancement algorithm. They imply the

application of not only methods from several image enhancement technique

categories but also image restoration methods [3]. This tends to increase the

computational cost but results in higher quality images.

Bazeille et al. [13] described a methodology composed of several

independent methods. First, spectral analysis filtering is applied to remove

repetitive wave patterns. Afterward, the image is scaled to a square power of two,

allowing the use of Fourier transform and Fast Fourier transform algorithms. The

color space is then transformed from RGB to YCbCr, making use of the luminance

(Y) channel only, where homomorphic filtering is applied to correct non-uniform

lighting. Afterward, wavelet noise reduction is used, and anisotropic filtering

smooths the image while preserving borders. Intensity is adjusted by suppressing

outlier pixels and stretching contrast. The image is then taken back to RGB space,

and the original size is reestablished. The last step is to normalize the color median

to ensure a balance between the three-color channels.

The procedure proposed by [23] is based on genetic algorithms and use the

Empirical Mode Decomposition (EMD) methodology, which is widely used for

non-linear data analysis. Through EMD, the image is decomposed into Intrinsic

Mode Functions (IMFs), with weights for each channel of the RGB model: red,

green, and blue. The improved image results from the combination of the IMFs with

weights calculated through the genetic component of the implementation. The

optimal individual is selected after processing the established generation, and then

a color correction method based on that of Bazeille et al. [13] is applied.

Table 1 depicts a set of works reported in the literature, detailing their

approaches and most relevant characteristics as strengths and weaknesses. Table 2

shows the most relevant sequences according to the number of citations in the

literature. This indicates that sequential-based approaches are effective to handle

contrast, noise, and lighting simultaneously in a similar way to [13].

756 A. Ceballos et al.

3 Methodology

To define the most appropriate sequence for the improvement of UIs, a set of

filters or procedures is established considering the three most important problems

present in a UI: lighting balance, contrast enhancement, and noise removal.

Based on the analysis reported in Tables 1 and 2, the most common procedures

in the literature are homomorphic filtering, CLAHE, and bilateral filtering to noise-

removal.

We determined CLAHE to be the pivotal step of the sequence due to its importance

as a contrast-enhancing method. Since it is known that applying contrast

enhancement methods can introduce artifacts in the image, noise removal was

determined to be the last step in the methodology. Thus, the sequence begins by

balancing lighting using homomorphic filtering, then contrast is enhanced through

CLAHE, and finally noise is removed by bilateral filtering (see Fig. 3).

Fig. 3: Proposed sequence.

Table 1. Some works reported and its main features.

Author Approach Features

[21] Utilization of consecutive filters. + Shows a need to apply noise reduction techniques after equalizing contrast.

+ Establishes Kovesi’s wavelet filtering as superior to anisotropic

filtering in automated implementations. [13] Utilization of consecutive filters,

alternating between RGB and YCbrCr

color models.

+ The algorithm is fully automatic and does not require adjustment

of parameters.

+ Positive results regarding the improvement of color and contrast. – The implementation is not optimal; it can be improved by a C

translation and better selection of filters.

[14] Contrast stretching in the RGB model and intensity and saturation stretching

in the HSI model.

+ Enhancement of both contrast and lighting. – The increase in noise after applying two stretching techniques is

not considered.

[16] Utilization of paired Markov random

fields to infer the true color of

underwater images.

+ Inference-based automatic enhancement of color and contrast.

– Requires and appropriately constructed training images dataset.

[23] Application of genetic algorithm to

improve images, treating RGB color

channels separately and using [13] for color correction.

+ Results are superior to [13] and other techniques such as contrast

stretching and histogram equalization.

– The genetic algorithm results in images with unbalanced color that must be corrected through other techniques.

[15] Comparison of manual and automatic

color correction using histogram equalization.

+ The manual technique shows favorable results in terms of MSE.

– No improvement is proposed for the automatic methodology.

Analyzing pre-processing filters sequences 757

Table 1. (Continued): Some works reported and its main features.

[22] Consecutive application of filters

followed by equalization of color and

contrast.

+ Positive results regarding noise elimination and contrast and lighting enhancement.

+ Best parameters for bilateral filtering determined, based on PSNR

(Peak Signal-to-Noise Ratio).

– Could not be compared with other techniques.

[17] Separate applications of contrast stretching to improve global contrast

and Rayleigh distribution-based

CLAHE to improve local contrast.

+ The use of Rayleigh-based CLAHE with a significance of 5% resulted in a remarkable improvement of MSE.

– Manual contrast stretching did not result in a large improvement

despite the time spent on its implementation.

[19] Separate implementations of retinex

model with WP/WW hybrid and ACE

to improve the balance of colors and contrast.

+ Removes color and/or lighting cast over the scene.

+ The ACE method results in images that are appropriate for

segmentation. – The WP/WW method did not prove effective.

+ Strength – Weakness

Table 2. List of sequences applied to the underwater-image enhancement

Year Author Implemented sequence

2002 [24] 1. CLAHE in RGB model. 2. Homomorphic filtering.

2005 [21] 1. Global contrast equalization. 2.a. Noise removal by wavelet. 2.b Noise

removal by anisotropic filtering.

2006 [13] 1. Removing Moiré effect. 2. Converting image to square proportions. 3.

Homomorphic filtering in YCrBr model.

4. Noise removal by wavelet in YCrBr model. 5. Anisotropic filtering in YCrBr

model. 6. Contrast stretching in YCrBr model. 7. Conversion of the image back

to original proportions. 8. Equalization of color means in RGB model.

2007 [14] 1. Contrast stretching in RGB model. 2. Contrast stretching in HSI model.

2010 [9] 1. Color correction in RGB model. 2. Contrast correction in RGB model. 3.

Contrast correction in HSV model.

2011 [25] 1. Weighed CLAHE. 2. Gaussian filtering.

2012 [22] 1. Homomorphic filtering. 2. Noise removal by wavelet. 3. Bilateral filtering.

4. Contrast stretching.

2013 [26] 1. CLAHE in RGB model. 2. CLAHE in HSV model. 3. The combination of

results from steps 1 and 2.

2014 [27] 1. Contrast correction. 2. Color correction.

2014 [28] 1. Homomorphic filtering. 2. Contrast stretching. 3. Noise removal by wavelet.

2015 [29] 1. Homomorphic filtering. 2. CLAHE in RGB model. 3. Gamma correction.

2015 [30] 1. CLAHE in RGB model. 2. Homomorphic filtering.

A homomorphic filter is a non-linear filter that allows the non-linear features of

an image to be converted into additive ones through Fourier transform to

manipulate. The motivation for the development of this technique is to handle

multiplicative and signal-dependent noise, but it has been successfully utilized in

digital image processing [31]. Ideally, the lighting component of an image is

uniform, while only the reflection component varies abruptly. However, in some

cases, such as UIs context, lighting is not constant. These variations can be modeled

as non-linear noise and treated through homomorphic filtering. This transformation uses a sequence of steps to apply the filters. The first step is to carry out a logarithmic

758 A. Ceballos et al.

space transform through natural logarithms. Fast Fourier transform is used to pass

into the frequency domain, and the chosen filter H is a high-pass filter to attenuate

the low-frequency components without damaging high-frequency information [32].

Once the image has been treated in frequency space, inverse fast Fourier transform

and the exponential function are used to return to the original space and obtain the

processed image (see Fig. 4).

Fig. 4: Steps of the homomorphic filter [32].

Contrast enhancement is often done through histogram equalization based on the

Cumulative Density Function. In a reduced contrast image, it is more likely that

certain intensity values will be found, and thus the function will not have a linear

shape. An image with good contrast is one where every intensity value in the

selected range has a similar representation. This is achieved using a mapping table,

which allows a more balanced apparition likeness to be assigned to every intensity

value [2]. Mapping values 𝑔 are determined for each intensity value f of the original

image as defined in (2), based on a maximum intensity 𝑔𝑚𝑎𝑥 and a minimum

intensity 𝑔𝑚𝑖𝑛, with 𝑃(𝑓) as a uniform probability density distribution.

𝑔 = [𝑔𝑚𝑎𝑥 − 𝑔𝑚𝑖𝑛] ∗ 𝑃(𝑓) + 𝑔𝑚𝑖𝑛 (2)

Although the increase in contrast is often noticeable, it often results in wrong

mappings for some colors, undesirable amplification of noise, and the appearance

of artifacts. The CLAHE algorithm solves this problem by combining two

techniques: Adaptive Histogram Equalization (AHE) and Contrast Limited

Histogram Equalization (CLHE).

The principle behind AHE is to divide the image into several tiles and increase

the contrast for each of these individually to improve the contrast locally rather than

globally. Once this step is finished, the image is reconstructed through linear

interpolation [33]. In CLHE, a parameter is established to limit the increase in

contrast by removing pixels from the highest points in the histogram and

redistributing them evenly throughout it [34]. Usually, this implies that the cut zone

surpasses the established threshold, so the process is repeated until the area above

the limit is small enough.

CLAHE not only combines AHE and CLHE to increase contrast locally in a

limited manner but also makes it possible to define a preferred probability density

distribution, thus also defining the desired shape of the histogram. Some examples

of these distributions are the exponential and Rayleigh distributions.

Bilateral filtering has the goal of reducing noise and enhancing borders in images

at the same time, operating in both the spatial and frequency domains. The

technique used often results in loss of detail in areas where transitions between

Analyzing pre-processing filters sequences 759

colors are softer, easing the use of segmentation techniques when compared to other

noise-removal and border-enhancing algorithms [35]. However, this filter is very

costly in computational terms.

The original bilateral filter implementation of [36], which can be used with both

gray-scale and color images, is employed with the recommended parameters of a

semi-width of 𝑤 = 5 and standard deviations for the spatial and frequency domains

of σspatial = 3 and σfrequency = 0,1.

3.1 Selection of CLAHE Parameters

The CLAHE parameters are NumTiles, Clip Limit, NBins, Range, Distribution,

Alpha, and color space. Due the different color spaces, we were interested about

analyzing its effect, so were selected: LAB, HSV, RGB-HSV, and Red-HSV like

as color spaces variants.

Parameter selection is carried out through experimental testing by verifying the

effectiveness of each parameter combination for each CLAHE color space variant.

The evaluation methodology was based on the principle that contrast improvement

should happen mainly over the area or object of interest in comparison with the

image background. For this reason, for each image, two windows are manually

selected: one focusing on the object of interest, subject to a high contrast increment,

and the other limited to background areas, where a noticeable change is not

expected when applying CLAHE.

The difference in average intensity 𝐶 between the original and treated windows

is defined as:

𝐶 =∑ [𝑅1(𝑖, 𝑗)].𝐼,𝐽

𝐼 ∗ 𝐽−∑ [𝑅2(𝑚, 𝑛)].𝑀,𝑁

𝑀 ∗ 𝑁 (3)

where 𝑅1 and 𝑅2 are the selected windows while 𝐼, 𝐽 and 𝑀,𝑁 are their respective

proportions.

The effectiveness of the contrast enhancement for the image 𝑘, 𝐸𝑘 ∈ [0,1] in each

CLAHE iteration is established as the sum of the differences of average intensities

calculated for the window with the object and the window with the background for

the original image 𝐶𝑜, and the improved image 𝐶𝑓 defined as follows:

𝐸𝑘 = 𝐶𝑜 − 𝐶𝑓 (4)

where 𝐶𝑜 is the difference in average intensity between the two windows for the

original image and 𝐶𝑓 is the difference in average intensity for the improved image.

The total effectiveness of the CLAHE method measured for a set of 𝑁 images is:

𝐸𝑡𝑜𝑡𝑎𝑙 =1

𝑁∑𝐸𝑖

𝑁

𝑖=1 𝑜

Once the CLAHE parameters for each color space variant have been determined,

the effectiveness of the algorithm sequences is analyzed from the perspectives

established in the literature: subjective and objective. The subjective approach

760 A. Ceballos et al.

considers color perception and visibility. The objective approach, is based on [9],

which determines the quality of the method according to some detected borders

with a border-detection algorithm and analysis of the histograms of the processed

images.

3.2 Objetive Evaluation Methodology

The two basic ideas behind objective approach is that a good enhance image

permits an improved border detection results, and that an image with contrast

enhance has a wide range histogram.

Border detection is carried out with the Sobel border-detection algorithm. This

method is a convolution technique where two kernels of uneven size are applied,

one for detecting vertical borders and the other for detecting horizontal ones. The

intensity gradient is calculated for the image at every point, showing how abrupt

the changes in intensity are, and therefore indicating the probability that a border

exists [37]. Eq. (5) defines both masks used in the algorithm, with ∆𝑥 being the

horizontal mask and ∆𝑦 the vertical one. The method is sensitive to noise, and its

selection was based on that fact, since in an image that has not been corrected

properly, not only the borders but also noise will be detected, so even if the border

count is favorable, an extremely high number of detected borders indicates that

some of them are noise or artifacts, so the image can be of poor quality and hard to

segment.

∆𝑥 = [−1 0 1−2 0 2−1 0 1

] ∆𝑦 = [1 2 10 0 0−1 −2 −1

]

(5)

Eq. (6) depicts the counting of separate elements in a binary image, thus:

𝑄 = { 𝑒𝑖 ∈ 𝐸 | 𝑒𝑖 ∩ 𝑒𝑗≠𝑖 = ∅ } (6)

where Q is the container including all separate elements in a binary image,

where an individual element 𝑒𝑖 is one that does not intersect with any other element

of the image.

Additionally, histogram analysis is done with the assumption that an image with

good contrast is one whose histogram covers a wide range of values. In an image

with poor contrast, however, most intensity values will be skewed towards either 0

(dark tonalities) or 255 (bright tonalities). Even if the histogram is not skewed in

either direction, its contrast is reduced too if the range of values covered is narrow.

The two measures used to determinate whether the histogram of the processed

image corresponds to an improved contrast are the difference between its maximum

and minimum values, and the standard deviation in the every intensity value, which

will be higher if there are color or intensity tones that are overrepresented and lower

if there is a balanced representation, indicating better contrast. The intensity

difference w of the histogram, and standard deviation σ are defined as:

Analyzing pre-processing filters sequences 761

𝑤 = 𝐼𝑚𝑎𝑥 − 𝐼𝑚𝑖𝑛 (7)

σ =1

1 − 𝑛∑(𝑥𝑖 − �̅�)

𝑛

𝑖=0

(8)

where 𝐼𝑚𝑎𝑥 and 𝐼𝑚𝑖𝑛 are the maximum and minimum intensity values, respectively,

and n is the highest possible intensity value, �̅� the average number of instances, and

𝑥𝑖 the number of instances for any given intensity value i.

4 Experimental Results

The images used were taken from the QUT image dataset [38] as well as other

smaller databases used in similar works [39]–[44]. The computer used for testing

was a desktop computer with an i7-4820k 3.70-GHz processor and 32 GB of RAM,

with code being executed in high-performance mode.

4.1 CLAHE Parameters

The estimation of the effectiveness of each combination of parameters was

done over a set of 30 images, and the results were averaged for each of 5000

iterations. The 5000 results were placed in descending order. The best results were

manually selected for every CLAHE variant, discarding parameter values where the

contrast increase resulted in extremely noisy images, such as NBins values higher

than 512, which distort color, or fixing the distribution as Rayleigh as recommended

for UI treatment [45], reducing the results to a subset of 2000. Table 3 shows the

parameters of the CLAHE algorithm as well as the evaluated values of each of them.

Although the best results were obtained by applying CLAHE on the LAB color

model, this does not guarantee that the use of LAB is a strictly superior option

compared to using models such as combined RGB-HSV. This is because

manipulating the luminance layer of the LAB color model does not have such a

noticeable effect on background areas as manipulating the red, green, and blue

layers of RGB, skewing the comparison and making it valid only for determining

the best parameters to use for each color model separately. Table 4 shows the

preferred parameter combination for each CLAHE color model variant.

4.2 Selection of CLAHE Variant and Measurement of Effectiveness

According to the results of the realized tests, in Table 5, three of the variants

of the original sequence of Homomorphic Filtering (HF), CLAHE, and Bilateral

Filter (BF) are observed positively, excluding only the Red-HSV implementation,

where CLAHE is applied on the red layer of the RGB color model and the hue and

saturation layers of the HSV color space.

Table 5 shows the average results for each measure of the execution of each variant

of the proposed sequence on 30 images, including the average execution times in

seconds.

762 A. Ceballos et al.

Table 3. List of CLAHE algorithm parameters

Parameter Description Evaluated values

NumTiles Size of the tile matrix [M N[ into which the image is divided for

processing, equal to M*N.

[6 6], [7 7], [8 8], [9 9], [10 10]

ClipLimit Scalar between 0 and 1 that limits contrast increase. Higher values

result in higher contrast but also more distortion of colors and

noise.

0.0025, 0.005, 0.01, 0.015, 0.02

NBins The number of binaries of the histogram used to build the

transformation for the image. A higher value results in a higher

dynamic range of colors but also higher processing cost and occasional color distortion.

64, 128, 256, 512, 1024

Distribution Adjusts the distribution used to give shape to the corrected

histogram.

‘Rayleigh’, ‘exponential.'

Alpha The parameter of the distribution, also called sigma in some

implementations.

0.3, 0.35, 0.4, 0.45, 0.5

Table 4. Best CLAHE parameters combination by color space

Color

model

Tiles Clip Limit Nbins Distribution Alpha Effectiveness # Ej. CLAHE

LAB [7 7] 0.02 512 Rayleigh 0.35 0.072102 1

HSV [6 6] 0.02 512 Rayleigh 0.5 0.057974 2

RGB-HSV [6 6] 0.02 256 Rayleigh 0.5 0.054282 5

RED-HSV [6 6] 0.02 256 Rayleigh 0.5 0.036053 3

Table 5. Sequence evaluation. Sequence Variation

in detected

borders

Average

width of

red

histogram

Average

width of

green

histogram

Average

width of

blue

histogram

Average

std dev.

of red

histogram

Average

std dev.

of green

histogram

Average

std dev.

of blue

histogram

Average

execution

time

(seconds)

Original

image

– 175.8438 203.0625 204.3125 1.4244 0.4976 0.5705 –

HF-LAB CLAHE-

BF

+63.9718% 203.3750 235.4375 226.3750 2.5168 0.3855 0.7441 18.7575

HF-HSV CLAHE-

BF

+96.3990% 212.6250 238.6250 239.5000 0.6406 0.3377 0.4031 18.4341

HF-HSV RGB

CLAHE-

BF

+19.5811% 182.1250 220.4375 214.3750 0.6975 0.3847 0.4752 18.5046

HF-Red

HSV

CLAHE-BF

–31.3528% 180.9375 197.1250 194.3125 0.7040 0.4560 0.5224 18.0022

Green: Best result.

Red: Worst result.

It can be observed that the most effective sequence for almost every evaluated

aspect is HF-HSV CLAHE-BF, with an augmentation of borders detected by means

of the Sobel technique and histogram width superior to that of the other sequences,

as well as lower standard deviation in the histograms for all the color layers, which

indicates higher contrast and color balance. The only aspect in which it is not the

superior implementation is the average execution time. However, the difference

from the others sequences is relatively small (less than 5%) and not significant when

compared to the execution time of the bilateral filter itself.

It is important to note that, as observed in Figs. 10 to 12, the HF-HSV

CLAHE-BF sequence shows a higher occurrence of noise, although subjective

Analyzing pre-processing filters sequences 763

analysis of the images also shows that the borders are better preserved. The HF-

RGBHSV CLAHE-BF results are not remarkable; it has neither the best result nor

the worst for any measurement. On the other hand, although its contribution to

border detection is not big, it generates less noise than the rest of the proposed

sequences. Furthermore, it is the only one of the four implementations that results

in improved color when images with a predominant color cast are processed. The

HF-LAB CLAHE-BF sequence produces histograms with a width very close to that

of its counterpart in the HSV model and detects borders with less noise but has the

worst average standard deviation for the red and blue histograms.

Finally, the HF-RedHSV CLAHE-BF implementation had the worst results

overall, with worse border detection and generation of narrower color histograms

when compared to the original images. The measurement of the average standard

deviation is not positive either for the red and green layers, confirming that it does

not result in images with improved contrast. The only positive point is its running

time, which is not an important advantage given the reduced difference in the rest

of the implementations.

5 Conclusions

In the field of UI enhancement, it is often difficult to implement techniques

designed for non-underwater contexts directly. Lighting inequalities, poor contrast,

and noise are problems when trying to implement computer vision technologies to

solve real world problems. In this case, the segmentation of the fish for

measurement implies the need for images without the mentioned issues, but such

images are difficult to obtain even in controlled environments.

Another of the hardest aspects is the measurement of the quality of the

processed images; usually, metrics such as the median square error and the peak

signal-to-noise ratio are applied, however, they cannot be used without having

ideal, undegraded images with which to compare the results or a model to generate

degraded images from them. Therefore, it becomes necessary to measure the

effectiveness of the implemented methods through histogram analysis and border

detection. Thus, it is important to define a model for the generation of degraded

images that simulate underwater conditions. Other metrics, such as the difference

in contrast between windows used to define the best CLAHE parameters, are

skewed when methods that are executed on different color spaces are compared.

The HF-HSV CLAHE-BF, HF-RGBHSV CLAHE-BF, and HF-LAB

CLAHE-BF sequences result in improved visibility, more balanced lighting, and

reduction of noise that could make segmentation harder. The processed images have

a wider range of colors that is more representative of the scene characteristics, with

more contrast between the distinct objects present in the scene, as confirmed by

Sobel border detection and histogram analysis. Subjectively, removal of color casts

is observed without the appearance of many noticeable artifacts when using

CLAHE on the combined RGB-HSV color model; this is not the case when CLAHE

764 A. Ceballos et al.

is executed on the HSV and LAB models, as the color is not directly manipulated.

However, HF-HSV CLAHE-BF is the best option when handling images where

there is no dominant color tonality.

Although not all the images used for testing were images of fishes in

controlled environments, the effectiveness of the proposed sequences in handling

scenes with noise in the form of particles and background elements is a good

indicator of progress towards the upcoming segmentation step to be carried out on

the processed images. Considering the advantages of using CLAHE on three of the

four proposed color models, future research should address the issue of

automatically selecting an adequate method depending on the attributes of the input

images. It is important to note, however, that the LAB and HSV results are similar

in that they are not capable of fully eliminating color casts typical of many

underwater environments. Considering that the HSV-based implementation has

better results than the LAB one, the automatic selection can be restricted to the HF-

HSV CLAHE-BF and HF-RGB HSV CLAHE-BF sequences.

In closing, the MATLAB implementation had the expected results. CLAHE

and the homomorphic filter did not have very large impacts on running time, even

when running the CLAHE algorithm five times when working with the combined

RGB-HSV model. On the other hand, even if bilateral filtering has a positive impact

on the segmentation of processed images, a real-time implementation will require

a faster implementation, possibly making use of programming languages such as C

or C++ and parallel processing techniques to obtain adequate running times.

a)

b)

c)

Analyzing pre-processing filters sequences 765

d)

e)

Fig. 10: (a) Original image, (b) image after HF-LAB CLAHE-BF processing, (c) image after HF-

HSV CLAHE-BF processing, (d) image after HF-RGB HSV CLAHE-BF processing, and (e)

image after HF-Red HSV CLAHE-BF processing. From left to right: the corresponding image,

border detection with n detected borders, and normalized color histogram.

766 A. Ceballos et al.

Fig. 11: To the left, three underwater images. To the right, the same three images after being

processed with the HF-CLAHE- HSV-BF sequence.

Analyzing pre-processing filters sequences 767

Fig. 12: To the left, three underwater images. To the right, the same three images after being

processed with the HF-CLAHE RGBHSV-BF sequence.

References

[1] B. Singh, R. S. Mishra, and P. Gour, Analysis of contrast enhancement

techniques for underwater image, Int. J. Comput. Technol. Electron. Eng., 1

(2011), no. 2, 190–194.

[2] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. Boston,

MA, USA: Addison-Wesley Longman Publishing Co., Inc., 1992.

[3] R. Wang, Y. Wang, J. Zhang, and X. Fu, Review on underwater image

restoration and enhancement algorithms, Proceedings of the 7th International

Conference on Internet Multimedia Computing and Service - ICIMCS '15,

(2015), 1–6. https://doi.org/10.1145/2808492.2808548

[4] S. Serikawa and H. Lu, Underwater image dehazing using joint trilateral

filter, Comput. Electr. Eng., 40 (2014), no. 1, 41–50.

https://doi.org/10.1016/j.compeleceng.2013.10.016

[5] J. R. Martinez-De Dios, C. Serna and A. Ollero, Computer vision and robotics

techniques in fish farms, Robotica, 21 (2003), no. 3, 233–243.

https://doi.org/10.1017/s0263574702004733

[6] J. Y. Chiang, Y.-C. Chen, and Y.-F. Chen, Underwater Image Enhancement:

Using Wavelength Compensation and Image Dehazing (WCID), Chapter in

Advanced Concepts for Intelligent Vision Systems, Vol. 6915, J. Blanc-Talon,

R. Kleihorst, W. Philips, D. Popescu and P. Scheunders, Eds. Berlin,

Heidelberg: Springer Berlin Heidelberg, 2011, 372–383.

https://doi.org/10.1007/978-3-642-23687-7_34

768 A. Ceballos et al.

[7] D. S. Baajwa, S. A. Khan and J. Kaur, Evaluating the Research Gaps of

Underwater Image Enhancement Techniques, Int. J. Comput. Appl., 117

(2015), no. 20, 19-23. https://doi.org/10.5120/20672-3369

[8] G. Padmavathi, P. Subashini, M. M. Kumar, and S. K. Thakur, Comparison

of filters used for underwater image pre-processing, IJCSNS International

Journal of Computer Science and Network Security, 10 (2010), no. 1, 58–65.

[9] K. Iqbal, M. Odetayo, A. James, Rosalina Abdul Salam and Abdullah Zawawi

Hj Talib, Enhancing the low quality images using Unsupervised Colour

Correction Method, 2010 IEEE International Conference on Systems, Man

and Cybernetics, (2010), 1703–1709.

https://doi.org/10.1109/icsmc.2010.5642311

[10] P. Sahu, N. Gupta and N. Sharma, A Survey on Underwater Image

Enhancement Techniques, Int. J. Comput. Appl., 87 (2014), no. 13, 19–23.

https://doi.org/10.5120/15268-3743

[11] M. S. Sowmyashree, S. K. Bekal, R. Sneha and N. Priyanka, A Survey on the

various underwater image enhancement techniques, Int. J. Eng. Sci. Invent.,

3 (2014), no. 5, 40–45.

[12] S. S. Thakare and A. Sahu, Comparative Analysis of Various Underwater

Image Enhancement Techniques, International Journal of Computer Science

and Mobile Computing, 3 (2014), no. 4, 33–38.

[13] S. Bazeille, I. Quidu, L. Jaulin and J.-P. Malkasse, Automatic underwater

image pre-processing, in CMM’06, 2006.

[14] K. Iqbal, R. Abdul Salam, A. Osman and A. Zawawi Talib, Underwater image

enhancement using an integrated colour model, Int. J. Comput. Sci., 34

(2007), no. 2, 529–534.

[15] N. bt Shamsuddin, W. F. bt Wan Ahmad, B. b Baharudin, M. Kushairi, M.

Rajuddin and F. bt Mohd, Significance level of image enhancement

techniques for underwater images, 2012 International Conference on

Computer & Information Science (ICCIS), (2012), 490–494.

https://doi.org/10.1109/iccisci.2012.6297295

[16] L. A. Torres-Méndez and G. Dudek, Color Correction of Underwater Images

for Aquatic Robot Inspection, Chapter in Energy Minimization Methods in

Computer Vision and Pattern Recognition, A. Rangarajan, B. Vemuri, and A.

L. Yuille, Eds. Springer Berlin Heidelberg, 2005, 60–73.

https://doi.org/10.1007/11585978_5

Analyzing pre-processing filters sequences 769

[17] P. N. Andono, I. K. E. Purnama and M. Hariadi, Underwater Image

Enhancement Using Adaptive Filtering for Enhanced Sift-Based Image

Matching, Journal of Theoretical & Applied Information Technology, 52

(2013), 273-280.

[18] T. Ji and G. Wang, An approach to underwater image enhancement based on

image structural decomposition, J. Ocean Univ. China, 14 (2015), no. 2, 255–

260. https://doi.org/10.1007/s11802-015-2426-2

[19] M. Chambah, D. Semani, A. Renouf, P. Courtellemont and A. Rizzi,

Underwater color constancy: enhancement of automatic live fish recognition,

16th Annual symposium on electronic imaging, 2003, Vol. 5293, 157–168.

[20] A. Mahiddine, J. Seinturier, J.-M. Boï, P. Drap and D. Merad, Performances

Analysis of Underwater Image Preprocessing Techniques on the

Repeatability of SIFT and SURF Descriptors, in WSCG 2012: 20th

International Conferences in Central Europe on Computer Graphics,

Visualization and Computer Vision, 2012.

[21] A. Arnold-Bos, J.-P. Malkasse, and G. Kervern, A preprocessing framework

for automatic underwater images denoising, European Conference on

Propagation and Systems, Brest, France, 2005.

[22] C. J. Prabhakar and P. U. P. Kumar, An Image Based Technique for

Enhancement of Underwater Images, ArXiv12120291 Cs, Dec. 2012.

[23] A. T. Çelebi and S. Ertürk, Empirical mode decomposition based visual

enhancement of underwater images, 2010 2nd International Conference on

Image Processing Theory Tools and Applications (IPTA), (2010), 221–224.

https://doi.org/10.1109/ipta.2010.5586758

[24] R. Garcia, T. Nicosevici and X. Cufi, On the way to solve lighting problems

in underwater imaging, OCEANS ’02 MTS/IEEE, Vol. 2, 2002, 1018–1024.

https://doi.org/10.1109/oceans.2002.1192107

[25] R. Chauhan and S. S. Bhadoria, An Improved Image Contrast Enhancement

Based on Histogram Equalization and Brightness Preserving Weight

Clustering Histogram Equalization, 2011 International Conference on

Communication Systems and Network Technologies (CSNT), (2011), 597–

600. https://doi.org/10.1109/csnt.2011.128

[26] W. Yussof, M. S. Hitam, E. A. Awalludin and Z. Bachok, Performing contrast

limited adaptive histogram equalization technique on combined color models

for underwater image enhancement, Int. J. Interact. Digit. Media, 1 (2013),

no. 1, 1–6.

770 A. Ceballos et al.

[27] A. S. Abdul Ghani and N. A. Mat Isa, Underwater image quality enhancement

through composition of dual-intensity images and Rayleigh-stretching,

SpringerPlus, 3 (2014), no. 1, 757. https://doi.org/10.1186/2193-1801-3-757

[28] X. Luan, G. Hou, Z. Sun, Y. Wang, D. Song and S. Wang, Underwater Color

Image Enhancement Using Combining Schemes, Mar. Technol. Soc. J., 48

(2014), no. 3, 57–62. https://doi.org/10.4031/mtsj.48.3.8

[29] K. Srividhya and M.M. Ramya, Performance analysis of pre-processing

filters for underwater images, 2015 International Conference on Robotics,

Automation, Control and Embedded Systems (RACE), (2015), 1–7.

https://doi.org/10.1109/race.2015.7097234

[30] M. Bhowmik, D. Ghoshal and S. Bhowmik, An improved method for the

enhancement of under ocean image, 2015 International Conference on

Communications and Signal Processing (ICCSP), (2015), 1739–1742.

https://doi.org/10.1109/iccsp.2015.7322819

[31] I. Pitas and A. N. Venetsanopoulos, Homomorphic Filters, Chapter in

Nonlinear Digital Filters, Springer US, 1990, 217–243.

https://doi.org/10.1007/978-1-4757-6017-0_7

[32] University of Patras, Enhancement by Point Processing, 29-Sep-2004.

[Online]. Available: http://www.upatras.gr/en [Accessed: 20-Apr-2017].

[33] N. M. Sasi and V. K. Jayasree, Contrast Limited Adaptive Histogram

Equalization for Qualitative Enhancement of Myocardial Perfusion Images,

Engineering, 05 (2013), no. 10, 326–331.

https://doi.org/10.4236/eng.2013.510b066

[34] R. C. Austin and A. Geselowit, Adaptive Histogram Equalization and Its

Variations, 1986.

[35] S. Paris, P. Kornprobst, J. Tumblin, and F. Durand, Bilateral Filtering: Theory

and Applications, Found. Trends® Comput. Graph. Vision, 4 (2008), no. 1,

1–75. https://doi.org/10.1561/0600000020

[36] C. Tomasi and R. Manduchi, Bilateral filtering for gray and color images,

Sixth International Conference on Computer Vision, (1998), 839–846.

https://doi.org/10.1109/iccv.1998.710815

[37] D. Ray, Edge Detection in Digital Image Processing, Univ. Wash. Dep.

Math., 2013.

Analyzing pre-processing filters sequences 771

[38] K. Anantharajah, ZongYuan Ge, Chris McCool, Simon Denman, Clinton

Fookes, Peter Corke, Dian Tjondronegoro, Sridha Sridharan, Local inter-

session variability modelling for object classification, 2014 IEEE Winter

Conference on Applications of Computer Vision (WACV), (2014), 309–316.

https://doi.org/10.1109/wacv.2014.6836084

[39] C. Ancuti, C. O. Ancuti, T. Haber, and P. Bekaert, Enhancing underwater

images and videos by fusion, 2012 IEEE Conference on Computer Vision and

Pattern Recognition (CVPR), (2012), 81–88.

https://doi.org/10.1109/cvpr.2012.6247661

[40] N. Carlevaris-Bianco, A. Mohan and R. M. Eustice, Initial results in

underwater single image dehazing, Oceans 2010 MTS/IEEE Seattle, (2010),

1–8. https://doi.org/10.1109/oceans.2010.5664428

[41] S. Emberton, L. Chittka and A. Cavallaro, Hierarchical rank-based veiling

light estimation for underwater dehazing, Procedings of the British Machine

Vision Conference 2015, (2015). https://doi.org/10.5244/c.29.125

[42] R. Fattal, Single image dehazing, ACM Trans. Graph. TOG, 27 (2008), no. 3,

72. https://doi.org/10.1145/1360612.1360671

[43] A. Galdran, D. Pardo, A. Picón and A. Alvarez-Gila, Automatic Red-Channel

underwater image restoration, J. Vis. Commun. Image Represent., 26 (2015),

132–145. https://doi.org/10.1016/j.jvcir.2014.11.006

[44] H. Lu, Y. Li, L. Zhang and S. Serikawa, Contrast enhancement for images in

turbid water, Journal of the Optical Society of America A, 32 (2015), no. 5,

886–893. https://doi.org/10.1364/josaa.32.000886

[45] MATLAB, “Contrast-limited adaptive histogram equalization (CLAHE) -

MATLAB adapthisteq,” 2006. [Online]. Available:

https://www.mathworks.com/help/images/ref/adapthisteq.html.

[Accessed: 26-Feb-2017].

Received: August 23, 2017; Published: September 28, 2017