Visual enhancement of underwater images using empirical mode decomposition

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orea Advanced Institute of S

cience and Technology

Visual enhancement of underwater images using Empirical Mode Decomposition

Kocaeli University Laboratory of Image and Signal Processing (KULIS), Electronics and Telecom. Eng. Dept., University of Kocaeli, 41040, Turkey

Expert Systems with Applications 39 (2012) 800–805

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1. Introduction Underwater Image

The main drawback of employing optical cameras in underwater applications is lim-ited visibility that can be restricted to about twenty meters in clear water and less than a few meters in turbid and coastal waters

1. Transmission Properties of light in waterAbsorption(light disappears)Scattering(light changes direction)

2. Depth of Waterabout 3m depth – red color disappears from 3m to 10m – orange color starts diminishingabout 10m – green goes offabout 25m – only blue color remains

3. Marine snow can produce bright artifact

Restriced Visibility,Non-uniform lighting.Low-contrast,Diminished color,Blurring of image features

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1. Introduction

Image Enhancement

1. Better interpretability, visibility and perception of object

2. Better contrast of image

3. Suppress the bluish / greenish color of the image

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1. Introduction / Overview

Empirical Mode Decomposition(EMD)

RGBImage

R

G

B

IMF1

IMF2

IMF3

IMF4

IMF1

IMF2

IMF3

IMF4

IMF1

IMF2

IMF3

IMF4

∑

w1

w2

w3

w4

w1

w2

w3

w4

w1

w2

w3

w4

Enhanced Image

Genetic Algorithm Color Correction Algorithm

N. E. Huang, 1998 N. E. Huang,2006 Mitchel, 1999

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1. Introduction

Image Enhancement

a. Original Imageb. Histogram equalization to R, G and B channels seperatelyc. Consrast stretchingd. Method presented in Bazeille at all. (2005)e. Proposed method in this paper

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Overview


RGBImage

R

G

B

IMF1

IMF2

IMF3

IMF4

IMF1

IMF2

IMF3

IMF4

IMF1

IMF2

IMF3

IMF4

∑

w1

w2

w3

w4

w1

w2

w3

w4

w1

w2

w3

Enhanced Image



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2. Empirical Mode Decomposition (EMD) Empirical Mode Decomposition (EMD)

1. Pioneered by N.E. Huang in 19982. Particularly suitable for the analysis of non-stationery and non-linear data3. Firstly defined for mono-dimensional signals4. Main idea: to represent a signal as a sum of components

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2. Empirical Mode Decomposition (EMD) PRINCI-

PLELet x(t) be a mono-dimensional signal:

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PLE1- Identify all local maxima of x(t):

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PLEDo the same thing with the local minima:

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PLE2- Interpolate between maxima ending up with some envelope :(t): e max

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PLELikewise for the minimal envelope : :)(min te

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PLE3. Comput the mean:2

maxmin (t)e(t)em(t)

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PLE4- Extract the detail d(t) = x(t) – m(t)

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PLE5- Iterate on the residual m(t) until the number of extrema in the signal is less than 2.

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PLEWe finally obtain the decomposition of the signal:

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PLEwe want to obtain by this decomposition are called Intrinsic Mode Functions (IMF) and they satisfy two conditions :

In the whole data set, the num-ber of extrema and the number of zero crossings must either equal or differ at most by one.

At any point, the mean value of the envelope defined by the lo-cal maxima and the envelope defined by the local minima is zero.

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PLEThe picture can be considered as a depth map in 2D:

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2. Empirical Mode Decomposition (EMD) PROCESS

1. Find all points of 2D local maxima and all points of 2D local minima of inputlk(i, j) .

2. Produce the upper envelope (emax(i, j)) by 2D spline interpolation of local max-ima and the lower envelope (emin(i, j)) by 2D spline interpolation of local minima.

3. Compute the mean of the upper and lower envelopes: e_meanlk(i, j) = (emax(i, j) + emin(i, j))/2.

4. Subtract the envelope mean from the input signal: hlk(i, j) = inputlk(i, j) ─ e_meanlk(i, j).

5. Iterate with hlk(i, j) as input

6. If the residue does not contain any more extreme points the EMD decomposition process is terminated

7. The residue signal Rl(i, j) is computed as Rl(i, j) = inputl1(i, j) ─ IMFl(i, j)

Stopping Criterion =

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2. Empirical Mode Decomposition (EMD) EMD PROCESSED IMAGE

(a)Original image(b)Green channel(c) First IMF(d)Second IMF(e)Third IMF(f) Fourth IMF(g)Final residue

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Overview


RGBImage

R

G

B

IMF1

IMF2

IMF3

IMF4

IMF1

IMF2

IMF3

IMF4

IMF1

IMF2

IMF3

IMF4

∑

w1

w2

w3

w4

w1

w2

w3

w4

w1

w2

w3

Enhanced Image



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3. Genetic Algorithm for Weights OPTIMATIZATION

Obtain the weight set automatically in an optimum way

Maximization 1. Average gradient

Contrast of the reconstructed Image and thus reflects trhe clarity of the final Image

2. Sum of entrophy

Richness of information in an image

Objective Function

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3. Genetic Algorithm for Weights GA IMPLEMENTA-

TION1. Generate the initial population randomly

2. For each individual, calculate the objective function Evaluate the fitness of the current solution

3. Select individual from the population as parent

4. Generate children from parents by applying crossover and mu-ation

5. Replace the current population with the children to form the next generation

6. Check stop criterion. The stop criterion used in this paper is the

total number of generations. If the stop criterion is not met, return to step 3.

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3. Genetic Algorithm for Weights

Initial Popuation randomly (W: 0~1)

Objective Function

Fitness value

Objective Function

Good Fitness value

Roulette wheel method

Generate children(new population)

Crossover and Mutation

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4. Color correction

– To suppress prominent blue or green colors. (to compensate for incorrect color balance)

– Use a color correction algorithm in Bazeille et al. (2006)

– The color correction algorithm simply adds the difference between a desired mean value

ICC(i, j) : result after color correctionI(i, j) : pixel value of image for R, G, B channels separately at the (i, j)location

μ : mean value of the corresponding channel

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4. Color correction

Initial Popuation randomly (W: 0~1)

Roulette wheel method

Generate children(new population)

Crossover and Mutation

Color correction

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4. Color correction

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5. Experiment and the results

Image Enhancement1. Better interpretability, visibility and perception of object2. Better contrast of image3. Suppress the bluish / greenish color of the image

a. Original Imageb. Histogram equalization to R, G and B channels seperatelyc. Consrast stretchingd. Method presented in Bazeille at all. (2005)e. Proposed method in this paper

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Origin

al im

age

Prop

osed

app

roac

h

Contra

st st

retc

hing

Histog

ram

equ

alizat

ion

Bazei

lle e

t al.,

200

60

1

2

3

4

5

6

7

8

Entropy

Original image

Proposed approach

Contrast stretching

Histogram equalization

Bazeille et al., 2006

0

1

2

3

4

5

6

7

Average gradientContrast and clarity Richness of informa-

tion

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In GA implementation each population consists of 50 individuals; a crossover rate of 8 and a mutation rate of 6 is used

High local sparialFrequency High Weight

Low local sparialFrequency Low Weight

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6. Reference

1. Study of the Empirical Mode Decomposition and Application to Pictures

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Myeong, Wancheol

Thank you

Visual enhancement of underwater images using Empirical Mode Decomposition

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