Marine Snow Removal in Underwater Images

Bogdan Smolka and Monika Mendrela

Silesian University of Technology, Akademicka 16, Gliwice, Poland

Keywords:

Marine Snow, Image Enhancement, Noise Reduction.

Abstract:

In this paper two methods of marine snow detection in underwater images are presented. The proposed tech-

niques are based on the pixel corruption measures which enable the identiﬁcation of clusters forming the

marine snow. As the detection of marine snow contaminating the images must be followed by an inpainting

step, various techniques which allow to restore the images with missing regions were evaluated. The experi-

ments revealed that the restoration quality of applied inpainting techniques is dependent on the image structure

and the size of regions needed to be restored and that their overall efﬁciency is comparable. Therefore, the

faster algorithms should be preferred. To asses the quality of marine snow removal techniques, a database of

images with 5 levels of contamination was created. The experiments performed on this database showed that

the proposed marine snow detection techniques coupled with fast inpainting methods yield very satisfactory

results, superior to the techniques already known from the literature.

1 INTRODUCTION

Due to numerous factors contributing to the degrada-

tion of underwater images, the problem of their en-

hancement has received a lot of attention. Poor light-

ing conditions frequently have a negative impact on

underwater image acquisition. The underwater im-

ages can be of low contrast, they are frequently blurry

or hazy, have severely distorted chrominance chan-

nels, or even have completely lost their color. The nat-

ural occurrence of so called marine snow (MS), which

is an accumulation of biogenic material falling down

from the upper layers of the water column, is also a

signiﬁcant source of underwater images distortions.

As plants and animals perish and decompose, ma-

rine snow grows as a result of the aggregation process

up to several centimeters in diameter. The particles

are made up of faecal matter, sand, soot and other

inorganic materials and the reﬂected light appear as

bright spots obscuring the underwater image scene

orgens et al., 2015; Alldredge and Silver, 1988).

Marine snow is typically regarded as a source of

noise that should be eliminated from the image to pre-

vent a decline in the efﬁciency of image processing

operations like object segmentation, classiﬁcation or

recognition. Due to the fact that the ﬂoating particles

frequently resemble the seabed’s structures, such as

small stones and the textures of plants and animals,

the problem of MS detection and removal is challeng-

ing. In addition, the pixels that represent the detected

snowﬂakes should be eliminated in a way that pre-

vents artifacts from being introduced and rendering

image analysis systems less effective.

In this work, two effective techniques for detect-

ing marine snow are described and the efﬁcacy of

various image inpainting methods which are used

to replace the noisy pixels are examined. The pro-

posed techniques of underwater image enhancement

affected by marine snow proved to be reliable and ef-

fective and can be used in real-time applications.

2 MARINE SNOW REMOVAL

Marine snow varies in size, shape and brightness.

However, mostly it appears as bright, almost white

spots, randomly distributed over the image. The spots

can be of different size, but usually they are repre-

sented by circular clusters of up to 50 × 50 pixels, de-

pending on the image resolution. Their shape resem-

bles to some extent the Gaussian distribution, with in-

tensity decreasing with the distance to the peak loca-

tion. However, the maximum of intensity can be also

located outside of the spot center and very often the

snowﬂakes are surrounded by a dark area which high-

lights their intensity.

The process of marine snow removal can be di-

vided into two steps: detection of snow particles and

their replacement, preferably using a suitable image

Smolka, B. and Mendrela, M.

Marine Snow Removal in Underwater Images.

DOI: 10.5220/0011588200003332

In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022), pages 463-471

ISBN: 978-989-758-611-8; ISSN: 2184-3236

463

inpainting method. An efﬁcient algorithm of marine

snow removal should be accurate, computationally ef-

ﬁcient and should not create artifacts which could af-

fect the performance of computer vision analysis.

A straightforward method of marine snow re-

moval can rely on the reduced vector ordering. Us-

ing this approach, for each pixel in the local ﬁlter-

ing window, the distance in a chosen color space, to

other samples from this window is calculated. Then

the cumulated distances assigned to each pixel are

sorted and the pixel for which the sum of distances

is minimized, replaces the central sample of the ﬁl-

tering window (Astola et al., 1990). Deﬁned in this

way Vector Median Filter, (VMF) is able to remove

outliers and clusters of pixels like marine snow parti-

cles, which are treated as impulsive noise. However,

as the central pixel is uniformly replaced by a pixel

from the local window, this procedure leads to loss

of details and overall image blurring. Additionally,

if large spots of pixels are to be ﬁltered out, a sufﬁ-

ciently large processing window is needed to ensure

that the vector median, which is replacing the central

pixel of the window, does not belong to the unwanted

structures.

Quite often, instead of the vectorial processing of

the image structures, channelwise (marginal) methods

are applied. In this way, instead of the vector median,

the pixel whose components are medians of the re-

spective channel values of the samples from the pro-

cessing window is declared as the ﬁlter output. As

the correlation between color channels is neglected,

the marginal median ﬁlter output generally does not

belong to the set of pixels from the ﬁltering window,

which means that a new color is created. Such a pro-

cedure can generate artifacts, especially at the edges

of image objects, however it can be advantageous in

the case of Gaussian noise which affects all image

pixels.

To decrease the amount of pixels which are unnec-

essarily changed by the median ﬁlter, a switching pro-

cedure can be applied. First, the outlying pixels are

detected and then they are replaced by the local me-

dian. A simple measure of pixel corruption is the dif-

ference between the intensity of the central pixel of a

ﬁltering window and the median or weighted median

value (Sun and Neuvo, 1994). If the absolute differ-

ence exceeds a predeﬁned threshold, the pixel is de-

clared as noisy. When all image pixels are analyzed,

the noisy pixels in a ﬁltering window are replaced

with the median of uncorrupted samples in the pro-

cessing window, otherwise they remain unchanged.

This procedure can be applied iteratively, especially if

the noise contamination intensity is high (Zhou Wang

and Zhang, 1999).

(a) Test image 1 (b) Test image 2

(1) (2) (3) (4)

Figure 1: Color test images used for the evaluation of the

efﬁciency of inpainting methods.

To alleviate the problems caused by direct appli-

cation of image denoising algorithms, ﬁltering meth-

ods dedicated to marine snow removal were proposed.

In (Banerjee et al., 2014) a patch-based Adaptive

Probabilistic Approach (APA) was presented. First,

the image is converted from RGB into YCbCr color

space. Then the luminance channel is processed and

the pixels which intensity exceeds an adaptive thresh-

old, based on local mean and variance, are detected

and the probability of MS occurrence is computed. In

the sequel, to avoid object misclassiﬁcation, the pro-

cessing patch is enlarged and the calculations are re-

peated. If the existence of marine snow spot within

the processing window is conﬁrmed, the central pixel

is replaced with the local marginal median. Finally,

the image with reconstructed luminance channel is

converted back into the RGB color space.

Another approach, based on a Supervised Median

Filtering scheme (SMF), is focused on removing ma-

rine snow utilizing the characteristics of snowﬂakes

and applying a voting mechanism (Farhadifard et al.,

2017a). In the ﬁrst step, the algorithm detects the pix-

els belonging to a snowﬂake, analysing the distance

in the RGB color space to the remaining pixels of a

processing window. When the initial candidates are

found, then their local density is determined. The de-

tected pixels, which are grouped in clusters of low-

saturated pixels are declared as parts of a snowﬂake.

Pixel classiﬁcation is repeated for different window

ROBOVIS 2022 - Workshop on Robotics, Computer Vision and Intelligent Systems

464

sizes and ﬁnally the color pixels recognized as marine

snow are replaced with the marginal median of the

samples from the local area detected as background.

When comparing SMF to APA, this procedure of-

fers slightly better results, but still it does not solve the

problem of removing circle-shaped light reﬂections

caused by artiﬁcial illumination reﬂected from marine

snow particles. It also requires predeﬁning thresh-

olds and is time consuming due to window resizing

and voting scheme used for the pixels classiﬁcation.

Therefore, an improved method of marine snow re-

moval was proposed in (Farhadifard et al., 2017b).

The detection step remained unchanged, however to

replace the pixels detected as belonging to the ma-

rine snow, instead of median ﬁltering, an inpainting

method based on the Field of Experts concept (Roth

and Black, 2005) was utilized.

3 INPAINTING METHODS

The aim of image inpainting methods is the recon-

struction of its lost or damaged parts. Inpainting is

being used in many applications including removing

inscriptions and logos from images, eliminating the

red eye effect and restoration of old, damaged pho-

tographs, among many others. In this work we use

various inpainting methods to replace the image pix-

els detected as marine snow and we also validate their

performance in terms of objective quality measures.

The most computationally complex, but very ef-

fective technique, is the Annihilating ﬁlter based

Low-rank Hankel structured matrix completion ap-

proach, known as ALOHA (Jin and Ye, 2015). It

is a patch-based method which exploits the annihila-

tion property between a shift-invariant ﬁlter and im-

age data, observed in many inpainting algorithms.

Among the existing methods, there are many

which are based on Partial Differential Equations

(PDEs) of different order. An example is an optical

ﬂow based inpainting method called Absolutely Min-

imizing Lipschitz Extension (AMLE) (Caselles et al.,

1997). This technique solves PDE on the Riemannian

manifold for recovering missing values. Harmonic In-

painting via a Discrete Heat Flow algorithm (HARI)

(Shen and Chan, 2002) is also a method based on

solving a PDE.

Another technique called INAS belongs to a set

of algorithms based on sparse linear algebra and like

the mentioned algorithms it solves discrete PDEs

(D’Errico, 2004). Using this approach only horizon-

tal and vertical neighbours of the processed pixel are

considered. In the case of small missing elements,

this inpainting method works fast, however the pro-

cessing of each of the image channels is performed

separately.

Inpaitning via Iterative Process (INPAI) is a

method based on Discrete Cosine Transform (DCT)

and was created for automatic smoothing of multidi-

mensional incomplete data. It was adopted for the

purpose of supplementing large datasets of medical

or satellite images (Garcia, 2010). This procedure

is using the penalized least square (PLS) regression

formulated in terms of DCT. A statistical model is

created and the modeling process is completely con-

trolled by one smoothing parameter.

(1) (2) (3) (4)

PSNR [dB]

ALOHA AMLE HARI INAS INPAI MEAM MPR MSI

Test image 1

(1) (2) (3) (4)

PSNR [dB]

Test image 2

Figure 2: Comparison of inpainting results in terms of

PSNR quality measure using corrupted versions of test im-

age 1 and 2 shown in Fig. 1 c).

Modiﬁed Planar Rotator Model for Missing Data

Prediction (MPR) (

Zukovi

c and Hristopulos, 2018) is

anticipating the unknown values in the spatial data us-

ing Gibbs Markov Random ﬁeld. This model assumes

the use of spin interaction between closest neighbors

and is suitable for data sets of big sizes.

The Mumford-Shah Inpainting with Ambrosio-

Tortorelli Approximation (MSI) (Esedoglu and Shen,

2002) belongs to a group of variational models sim-

ulating the unique macro-inpainting mechanism. The

reconstruction of damaged parts of an image is per-

formed iteratively solving the Euler-Lagrange equa-

tions.

To increase the computational efﬁciency of the

inpainting procedure, a fast algorithm which recur-

sively ﬁlls in the missing pixels with the average of

the pixels with known values contained in the sliding

processing window was proposed (Smolka and Men-

drela, 2020). The elaborated Mean based Method

(MEAM) can be seen as a generalization of the

fast image inpainting algorithm proposed in (Oliveira

et al., 2001).

Marine Snow Removal in Underwater Images

465

Test Image 1 Corrupted Image (1) ALOHA AMLE HARI

INAS INPAI MEAM MPR MSI

Test Image 1 Corrupted Image (1) ALOHA AMLE HARI

INAS INPAI MEAM MPR MSI

Figure 3: Efﬁciency of the inpainting results using the color test images corrupted by pattern (1) shown in Fig. 1.

We conducted a study based on two images shown

in Fig. 1 of size 300 × 300, from which some parts

were removed. The regions of removed pixels, form-

ing 4 various patterns are marked with black color in

Fig. 1c). The most important, potentially decisive

factors were preservation of the natural appearance

and recovery of image details. In addition to the vi-

sual assessment, the inpainting results were analyzed

using the widely used PSNR image quality measure.

The efﬁciency of the evaluated methods in terms

of PSNR has been summarized in Fig. 2. It is worth

noticing that the differences in objective restoration

measures depend not only on the image structure but

also on the shape of the missing regions. The restora-

tion efﬁciency of the proposed method can be also as-

sessed visually in Fig. 3.

As can be observed very good results were ob-

tained using the ALOHA inpainting when restoring

the test images. The differences in the restoration

quality were smaller in case of the second test image,

which indicates that the efﬁciency of inpainting meth-

ods is dependent on the image structure and also on

the size of image regions which have to be ﬁlled. The

drawback of ALOHA is its huge computational com-

plexity which renders this method unsuitable for real

time applications. In contrast, the very fast MEAM

and INAS techniques proved to yield results compara-

ble with those offered by much more computationally

expensive methods.

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466

Figure 4: Exemplary images with increasing Marine Snow Intensity Level (MSIL).

MSIL 1 MSIL 2 MSIL 3 MSIL 4 MSIL 5

Figure 5: Marine Snow test images.

4 MARINE SNOW REMOVAL

As the ground truth images corrupted with marine

snow are not available, we marked manually all pix-

els depicting the MS particles in 15 carefully chosen,

representative images with resolution of 640 × 480

pixels. Figure 5 shows 3 exemplary images from the

dataset. These images were extracted from a video se-

quence from the Dataset of Marine Snow available at

http://underwaterchangedetection.eu, which contains

more than 1000 frames. In this way a set of 15 im-

ages with carefully marked marine snow particles was

created.

To obtain the ground truth images needed to per-

form experiments which aim was to reveal the efﬁ-

ciency of the various inpaiting methods, the missing

regions were restored using the ALOHA algorithm,

choosing parameter settings offering visually satisfy-

ing results. In this way, the restored images were

used as a reference set, which enabled to perform both

the objective and visual evaluation of the available in-

painting techniques.

The images with marked regions of marine snow

were used to simulate pictures with increasing con-

tamination intensity. To this end, the marked regions

(marine snow ﬂakes) from all images from the dataset

were superimposed on each of the 15 pictures restored

with ALOHA. In this way we created 15 × 15 = 225

images with added marine snowﬂakes.

To further increase the intensity of the marine

snow, we created a database consisting of images with

artiﬁcially superimposed MS regions taken from 2

different images from the dataset. The sources of

MS were randomly chosen 3 times for each image, so

that 45 noisy images were prepared. Additionally, for

each reference image, 3 corrupted sources containing

noise from 3, 4 and 5 other images from the dataset

were composed. In this way, we created a dataset of

5 marine snow contamination intensity levels, com-

posed of 225 + 4 × 45 = 405 images, with overlaying

marine snow. These images can be treated as contami-

nated by MS noise with available ground truth, so that

various denoising techniques can be evaluated. Figure

5 depicts examples of images with increasing marine

snow intensity levels. Below, for each noisy image

the superimposed snowﬂake regions are marked with

white color.

The results of the restoration of images corrupted

by marine snow obtained using the analyzed inpaint-

ing techniques are depicted in Fig. 6, which shows the

PSNR values of the enhanced images with increasing

amount of the marine snow. Again, the fast INAS and

Marine Snow Removal in Underwater Images

467

MEAM technique proved to yield results comparable

with the competitive inpainting techniques.

Having a database of images corrupted by artiﬁ-

cially created marine snow, we were able to create

and evaluate methods of its detection. The ﬁrst of the

elaborated MS detection techniquess is based on the

Ranked Order Absolute Difference (ROAD) statistic

(Garnett et al., 2005), which is used for determining

the degree of image pixels corruption. This method

will be referred to as ROAD based Algorithm for Ma-

rine Snow detection - RAMS.

The ROAD technique calculates the Euclidean

distance in the RGB color space between a processed

pixel and its neighbors contained in a ﬁltering win-

dow. Then the distances are sorted and a sum of

a speciﬁed number of smallest distances serves as a

measure of pixel similarity to its local neighborhood.

In this way the outlying pixels can be easily detected

and increasing the size of the ﬁltering window, clus-

ters of outliers can be identiﬁed. In this way, the

pixels which compose the marine snow are treated as

small pixel clusters which can be localized analyzing

the ROAD values.

AMLE HARI INAS INPAI MEAM MPR

1 2 3 4 5

MSIL

PSNR [dB]

Figure 6: Efﬁciency of the inpaiting methods applied to re-

store the underwater images corrupted with increasing Ma-

rine Snow Intensity Levels (MSIL) expressed with PSNR.

To increase the discriminating properties of the

proposed detector, the obtained map of pixel contam-

ination was subjected to the morphological Top-Hat

operation, which enables to diminish the inﬂuence of

image texture on the ﬁnal marine snow detection re-

sult. The detection step needs a thresholding value

which was obtained using the triangle binarization

method (G. Zack, 1977).

In this way, if the result of the Top-Hat operation

performed on the image depicting the ROAD values

exceeds the automatically determined threshold, then

a pixel is considered as belonging to a cluster of pixels

that make up a snowﬂake, which should be restored

using an inpainting technique. Otherwise, the pixel is

classiﬁed as background and is left unchanged. The

proposed method needs 2 parameters: the size r of

the processing window containing r × r pixels and the

number of smallest distances denoted as α, taken for

the calculation of ROAD based dissimilarity. The per-

formed experiments showed that satisfactory results

were obtained using r = α = 7.

The second method used for the detection of ma-

rine snow is based on the distance transformation and

will be called Distance Transform based Algorithm

for Marine Snow - DITAMS. This method determines

for every image pixel the minimal cost of a digital

path joining it with the boundary of a sliding process-

ing window.

The path cost is calculated as the sum of transi-

tions between adjacent pixels expressed through the

Euclidean distance in the RGB color space. Pixels

belonging to a cluster of pixels which differ signif-

icantly from their surrounding are not connected by

a low-cost path and therefore they can be easily de-

tected using the triangle binarization technique. To

speed up the process of distance transform calcula-

tion, a two-pass algorithm proposed by Rosenfeld and

Pfaltz has been applied (Rosenfeld and Pfaltz, 1968).

Figure 7 shows a test image and the binarization

result using the triangle algorithm. The results ob-

tained using the RAMS method are visually very sim-

ilar. The performed experiments revealed that sat-

isfying efﬁciency of the proposed DITAMS scheme

was obtained for the window size r = 7, which makes

the proposed scheme fast enough to be applied in real

time MS detection systems.

Figure 8 compares the Accuracy, Precision, Re-

call and Speciﬁcity of the APA, SMF, RAMS and

DITAMS marine snow detection techniques. To de-

scribe the efﬁciency measures, the following notation

is used: Q - total number of image pixels, P - num-

ber of noisy pixels, N - number of undisturbed pixels

(Q=P+N), TP - pixels correctly classiﬁed as noise, TN

- pixels correctly classiﬁed as uncorrupted, FP - pixels

which were not correctly classiﬁed as noise.

Accuracy is a basic measure, which refers to the

ratio of the number of correctly classiﬁed pixels in an

image to their total number - (TP+TN)/Q. The Accu-

racy values are high for all of the analyzed methods

but the best results were obtained using DITAMS and

RAMS.

Precision speciﬁes the ratio of the number of pix-

els correctly classiﬁed to the class of noisy pixels in

relation to all pixels classiﬁed as noise, which in-

cludes misclassiﬁcations as well - TP/(TP+FP). The

experiments showed that the DITAMS was the most

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468

(a) Test image

(b) Binarized distance map

Figure 7: Binarization of the output of the distance trans-

form based marine snow detector.

precise among the evaluated methods. It can be also

concluded that the APA and SMF methods are not the

best choice if high precision of MS detection is re-

quired.

Next measure describing the efﬁciency of the pro-

posed techniques is Recall, which speciﬁes the ratio

of the number of pixels correctly classiﬁed as noise

to all noisy pixels in the image - TP/P. The highest

value of this parameter is obtained for the RAMS al-

gorithm. The APA method is not much worse and is

followed by SMF and DITAMS. The lower values of

this parameter is caused by the high number of incor-

rectly classiﬁed pixels, which means that in the detec-

tion stage, there are a lot of pixels which belong to the

marine snowﬂakes but are classiﬁed as background.

Last measure of the efﬁciency of the compared

methods is the Speciﬁcity, (true negative rate) - TN/N,

which speciﬁes the ratio of the pixel number correctly

classiﬁed as not belonging to the MS to the number of

background pixels. For all the compared techniques,

speciﬁcity measure achieves very high values. For

the DITAMS detection method it is almost equal to

1 which indicates that in this respect the algorithm

APA SMF RAMS DITAMS

1 2 3 4 5

0.96

0.97

0.98

0.99

ACCURACY

1 2 3 4 5

0.2

0.4

0.6

0.8

PRECISION

1 2 3 4 5

0.4

0.6

0.8

RECALL

1 2 3 4 5

0.97

0.98

0.99

MSIL

SPECIFICITY

Figure 8: Boxplots summarizing the performance of 4 de-

tection methods for the increasing marine snow intensity

levels (MSIL).

Marine Snow Removal in Underwater Images

469

APA SMF RAMS+MEAM

RAMS+INAS DITAMS+MEAM DITAMS+INAS

1 2 3 4 5

MSIL

PSNR [dB]

Figure 9: Efﬁciency of MS removal evaluated using PSNR

quality measure for increasing marine snow intensity levels.

works almost perfectly.

Figure 9 depicts the efﬁciency of the MS detec-

tion and removal through the application of MEAMS

and INAS inpainting methods in terms of PSNR mea-

sure. As can be observed the proposed RAMS and DI-

TAMS methods yield comparable results, which are

much better than those offered by the APA and SMF.

Additionally, the plots show that the fast MEAM in-

painting algorithm provides results similar to those

achieved using the INAS interpolation, which make

the proposed algorithms very attractive, especially in

the case of real-time applications.

Figure 10 exhibits results of marine snow removal

using the APA and SMF methods compared with

those obtained with the combination of RAMS and

DITAMS detection methods and MEAM and INAS

inpainting techniques. As can be observed the ma-

rine snow particles are removed and the image is

well restored using the novel, proposed techniques.

As both the MS detection techniques and inpainting

methods are computationally efﬁcient, the new en-

hancement frameworks can be applied for real time

imaging tasks.

5 CONCLUSIONS

In this paper two novel methods of marine snow de-

tection and their removal with the use of fast inpaint-

ing methods have been proposed. The ﬁrst one is

based on the ROAD measure of pixel impulsiveness

and the second is determining the cost of a connec-

tion between the central pixel and the boundary of

the ﬁltering window. The quality of inpainting tech-

Noisy image MSIL 5

APA, 38.9 dB SMF, 40.6 dB

RAMS+MEAM, 44.0 dB RAMS+INAS, 44.2 dB

DITAMS+MEAM, 43.5 dB DITAMS+INAS, 43.6 dB

Figure 10: Result of marine snow removal expressed with

PSNR obtained for exemplary MSIL 5 image.

niques, which were applied to restore the underwater

images with detected clusters of pixels forming the

marine snow particles, was evaluated using the objec-

tive PSNR quality measure and the results were also

assessed visually. Extensive experiments conﬁrmed

that the developed marine snow techniques coupled

with the fast MEAM and INAS inpainting methods

offer better image restoration results than the algo-

rithms already known from the literature. The devel-

oped techniques are computationally inexpensive and

can be applied in real time applications. Although the

elaborated algorithms are intended for marine snow

removal, they can be applied in various applications

in which the detection and inpainting of small clus-

ters of pixels is required.

ROBOVIS 2022 - Workshop on Robotics, Computer Vision and Intelligent Systems

470

ACKNOWLEDGEMENTS

This work was supported by the Silesian Univer-

sity of Technology, Poland, (grant BK 2022) and

the Polish National Science Centre under the project

2017/25/B/ST6/02219.

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