under water image enhancement

15
ENHANCEMENT OF UNDER WATER IMAGES Dept Of E&C, NMAMIT Abstract The underwater image processing area has received considerable attention within the last decades, showing important achievements. The underwater images are essentially characterized by their poor visibility because light is exponentially attenuated as it travels in the water and the scenes result poorly contrasted and hazy. When we go deeper,colors drop off one by one depending on their wavelenghth, blue color travels the longest in the water due to its shortest wavelength, making the underwater images to be dominated essentially by blue color. In summary,the images suffer from limited range visibility, low contrast, non uniform lighting, blurring, color diminished (bluish appearance) and noise. In order to improve the perception, underwater images are post processed to enhance the quality of the image. In this project, a denoising method is used for removing additive noise present in the underwater images. Image denoising using the wavelet transform has been attracting much attention. Wavelet based approach provides a particularly useful method for image denoising when the preservation of image features in the scene is of importance. In the proposed denoising method, first homomorphic filtering is used for correcting non uniform illumination, and then anisotropic filtering is used for smoothing. After smoothing, wavelet subband threshold with Modified BayesShrink function is applied. The whole of the algorithm used in this project is to be coded in MATLAB. A GUI is to be developed in MATLAB to showcase the processed image after every step.

Upload: madhu-nole

Post on 16-Apr-2015

246 views

Category:

Documents


3 download

DESCRIPTION

enhances under water images

TRANSCRIPT

Page 1: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

Abstract

The underwater image processing area has received considerable attention within the last

decades, showing important achievements. The underwater images are essentially

characterized by their poor visibility because light is exponentially attenuated as it travels in

the water and the scenes result poorly contrasted and hazy. When we go deeper,colors drop

off one by one depending on their wavelenghth, blue color travels the longest in the water

due to its shortest wavelength, making the underwater images to be dominated essentially by

blue color. In summary,the images suffer from limited range visibility, low contrast, non

uniform lighting, blurring, color diminished (bluish appearance) and noise. In order to

improve the perception, underwater images are post processed to enhance the quality of the

image.

In this project, a denoising method is used for removing additive noise present in the

underwater images. Image denoising using the wavelet transform has been attracting much

attention. Wavelet based approach provides a particularly useful method for image denoising

when the preservation of image features in the scene is of importance. In the proposed

denoising method, first homomorphic filtering is used for correcting non uniform

illumination, and then anisotropic filtering is used for smoothing. After smoothing, wavelet

subband threshold with Modified BayesShrink function is applied.

The whole of the algorithm used in this project is to be coded in MATLAB. A GUI is to be

developed in MATLAB to showcase the processed image after every step.

Page 2: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

CHAPTER 1

1.1 INTRODUCTION

Underwater images are essentially characterized by their poor visibility because light is

exponentially attenuated as it travels in the water and the scenes result poorly contrasted and

hazy. Light attenuation limits the visibility distance at about twenty meters in clear water and

five meters or less in turbid water. The light attenuation process is caused by absorption and

scattering. The absorption and scattering process of the light in water influence the overall

performance of underwater imaging systems. Forward scattering generally leads to blurring

of the image features. On the other hand, backscattering generally limits the contrast of the

images. Absorption and scattering effects are not only due to the water itself but also due to

the components such as dissolved organic matter[4].

The visibility range can be increased with artificial illumination of light on the object but it

produces non-uniform of light on the surface of the object and producing a bright spot in the

center of the image with poorly illuminated area surrounding it. The amount of light is

reduced when we go deeper, colors drop off depending on their wavelengths. The blue color

travels the longest in the water due to its shortest wavelength[2]. Underwater image suffers

from limited range visibility, low contrast, non-uniform lighting, blurring, bright artifacts,

color diminished and noise.

In this project, wavelet based image denoising technique is used for removing additive noise

in the underwater images. Before applying the wavelet shrinkage function, first homomorphic

filtering is used to correct non-uniform illumination of light. Homomorphic filter

simultaneously normalizes the brightness across an image and increases contrast. The

homomorphic filtering performs in the frequency domain and it adopts the illumination and

reflectance model. After correcting non uniform illumination using homomorphic filtering,

anisotropic filtering is used to smooth the image in homogeneous area but preserve image

features and enhance them. Finally, applying wavelet denoising technique to denoise the

image. Wavelet based image denoising techniques are necessary to remove random additive

Gaussian noise while retaining as much as possible the important image features. The main

objective of these types of random noise removal is to suppress the noise while preserving the

original image details. Especially for the case of additive white Gaussian noise a number of

techniques using wavelet-based thresholding have been proposed. In the recent years there

has been a fair amount of research on wavelet thresholding and threshold selection for image

Page 3: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

denoising, because wavelet provides an appropriate basis for separating noisy signal from the

image signal.

1.2 Literature survey

Problems in underwater images

A major difficulty to process underwater images comes from light attenuation. Light

attenuation limits the visibility distance, at about twenty meters in clear water and five meters

or less in turbid water. The light attenuation process is caused by the absorption (which

removes light energy) and scattering (which changes the direction of light path)[.

Absorption and scattering effects are due to the water itself and to other components such as

dissolved organic matter or small observable floating particles[4].

Not only the amount of light is reduced when we go deeper but also colors drop off one by

one depending on the wavelength of the colors. Red color disappears at the depth of 3m.

Secondly, orange color starts disappearing while we go further. At the depth of 5m,the orange

color is lost. Thirdly most of the yellow goes off at the depth of 10m and finally the green and

purple disappear at further depth[2]. This is shown diagrammatically in Figure 1.1. The blue

color travels the longest in the water due to its shortest wavelength. This is reason which

makes the underwater images having been dominated only by blue color. In addition to

excessive amount of blue color, the blur images contain low brightness and low contrast.

Figure 1.1 Color appearance in underwater

In this project, a denoising method is used for removing additive noise present in the

underwater images. Image denoising using the wavelet transform has been attracting much

Page 4: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

attention. Wavelet based approach provides a particularly useful method for image denoising

when the preservation of image features in the scene is of importance[6].

Wavelet analysis is an exciting new method for solving difficult problems in mathematics,

physics, and engineering, with modern applications as diverse as wave propagation, data

compression, signal processing, image processing, pattern recognition, computer graphics,

the detection of aircraft and submarines and other medical image technology. Wavelets allow

complex information such as music, speech, images and patterns to be decomposed into

elementary forms at different positions and scales and subsequently reconstructed with high

precision. Signal transmission is based on transmission of a series of numbers. The series

representation of a function is important in all types of signal transmission. The wavelet

representation of a function is a new technique. Wavelet transform of a function is the

improved version of Fourier transform. Fourier transform is a powerful tool for analyzing the

components of a stationary signal. But it is failed for analyzing the non stationary signal

where as wavelet transform allows the components of a non-stationary signal to be analyzed.

In 1982 Jean Morlet a French geophysicist, introduced the concept of a `wavelet'. The

wavelet means small wave and the study of wavelet transform is a new tool for seismic signal

analysis. Immediately, Alex Grossmann theoretical physicists studied inverse formula for the

wavelet transform. The joint collaboration of Morlet and Grossmann yielded a detailed

mathematical study of the continuous wavelet transforms and their various applications, of

course without the realization that similar results had already been obtained in 1950's by

Calderon, Littlewood, Paley and Franklin. However, the rediscovery of the old concepts

provided a new method for decomposing a function or a signal.

Some Advantages of Wavelet Theory

a) One of the main advantages of wavelets is that they offer a simultaneous localization in

time and frequency domain.

b) The second main advantage of wavelets is that, using fast wavelet transform, it is

computationally very fast.

c) Wavelets have the great advantage of being able to separate the fine details in a signal.

Very small wavelets can be used to isolate very fine details in a signal, while very large

wavelets can identify coarse details.

d) A wavelet transform can be used to decompose a signal into component wavelets.

Page 5: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

e) In wavelet theory, it is often possible to obtain a good approximation of the given function

f by using only a few coefficients which is the great achievement in compare to Fourier

transform.

f) It can often compress or de-noise a signal without appreciable degradation.

Page 6: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

Chapter 2

2.1 Objective of the project

- To correct non uniform illumination and to enhance contrasts in the image.

- To suppress noise which is inherent in underwater images.

- To smooth the images while preserving the edges in the image.

- Suppressing the predominant blue color by equalizing the RGB channels.

2.2 Block Diagram

Figure 2.1 shows the block schematic of proposed project

Figure 2.1 block schematic of proposed project

2.2.1 Conversion from RGB to YCbCr

This color space conversion allows us to work only on one channel instead of processing the

three RGB channels.

In YCbCr color space we process only the luminance channel (Y) corresponding to intensity

component (gray scale mage). This step speeds up the processing avoiding to process each

time each RGB channels[4].

Conversion from RGB to YCbCr:

Y=0.299(R-G) + G + 0.114(B-G)

Cb=0.564(B-Y)

Cr=0.713(R-Y)

Input Image Conversion from RGB to

YCbCr

Homomorphic Filtering

Anisotropic Filtering

Denoising Conversion

from YCbCr to RGB

Equalising Color Mean

Enhanced & denoised image

Page 7: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

2.2.2 Homomorphic Filtering

The homomorphic filtering is used to correct non uniform illumination and to enhance

contrasts in the image. It is a frequency filtering, preferred to others techniques because it

corrects non uniform lighting and sharpens the edges at the same time.

Homomorphic filter is used for image enhancement. It simultaneously normalizes the

brightness across an image and increases contrast. Here homomorphic filtering is used to

remove multiplicative noise. Illumination and reflectance are not separable, but their

approximate locations in the frequency domain may be located. Since illumination and

reflectance combine multiplicatively, the components are made additive by taking the

logarithm of the image intensity, so that these multiplicative components of the image can be

separated linearly in the frequency domain. Illumination variations can be thought of as a

multiplicative noise, and can be reduced by filtering in the log domain [4].

To make the illumination of an image more even, the high frequency components are

increased and low-frequency components are decreased, because the high frequency

components are assumed to represent mostly the reflectance in the scene (the amount of light

reflected off the object in the scene), whereas the low frequency components are assumed to

represent mostly the illumination in the scene. That is, high-pass filtering is used to suppress

low frequencies and amplify high frequencies, in the log-intensity domain.

The basic nature of the image f(x,y) may be characterized by two components:

1. The amount of source light incident on the scene being viewed, and

2. The amount of light reflected by the objects in the scene. These portions of light are called

the illumination and reflectance components, and are denoted i(x,y) and r(x,y) respectively.

The functions i and r combine multiplicatively to give the image function f

ƒ(x,y)=i(x,y).r(x,y)

where 0< i(x,y)<infinity and 0<r(x,y)<1 We cannot easily use the above product to operate

separately on the frequency components of illumination and reflection because the Fourier

transform of the product of two functions is not separable; that is

F[ƒ(x,y)]≠F[i(x,y)].F[r(x,y)]

However, suppose that we define

z(x,y)= ln ƒ(x,y)

= ln i(x,y)+ ln r(x,y)

Now applying the fourier transform for the above equation results

F[z(x,y)] = F[ln ƒ(x,y)]

Page 8: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

= F{ln i(x,y)}+F{ln r(x,y)}

or

Z(u,v) =Fi(u,v) + Fr(u,v)

where Z, Fi and Fr are the Fourier transforms of z, ln i and ln r respectively.

The function Z represents the Fourier transform of the sum of two images: a low frequency

illumination image and a high frequency reflectance image.

If we now apply a filter with a transfer function that suppresses low frequency components

and enhances high frequency components, then we can suppress the illumination

component and enhance the reflectance component. Thus

S(u,v) =H(u,v)Z(u,v)

= H(u,v)Fi(u,v) +H(u,v) Fr(u,v)

where S is the Fourier transform of the result with

H(u,v)= (rH-rL)(1-exp(-(u2+v

2)/2 2

))+rL

where rH = 2.5 and rL = 0.5 are the maximum and minimum coefficients values and a

factor which controls the cutoff frequency. These parameters are selected empirically.

Computations of the inverse Fourier transform to comeback in the spatial domain and then

taking the exponent to obtain the filtered image.

2.2.3 Anisotropic Filtering

These filters smooths the image in homogeneous area, preserves image features and enhance

them. It reduces the blurring effect. It follows an algorithm to calculate nearest-neighbour

differences and diffusion coefficient to modify the pixel value (proposed by Perona and

Malik [3]). This algorithm is automatic so it uses constant parameters selected manually.

Computation of the nearest-neighbour differences and computation of the diffusion

coefficient in the four directions North, South, East, West. Many possibilities exist for this

calculation, the easiest way is as follows:

NIi,j = Ii-1,j - Ii,j, cNi,j = g(| NIi,j|)

SIi,j = Ii+1,j - Ii,j, cSi,j = g(| SIi,j|)

EIi,j = Ii,j+1 - Ii,j, cEi,j = g(| EIi,j|)

WIi,j = Ii,j-1 - Ii,j, cWi,j = g(| WIi,j|)

Page 9: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

where the function g is defined as: g(| I|) =

and with K set to 0.1.

Modification of the pixel value using below equation

Ii,j = Ii,j+ [cN . NI + cS . SI + cE . EI + cW . WI ]i,j

With 0

2.2.5 Denoising

Wavelet based image denoising techniques are necessary to remove random additive

Gaussian noise while retaining as much as possible the important image features. The main

objective of these types of random noise removal is to suppress the noise while preserving the

original image details.

In addition to scattering and absorption effects, macroscopic floating particles producing

images of the size of a pixel can be present as well: it may be, for instance, sand raised by the

motion of a diver, or small plankton particles. These particles are part of the scene, but cause

generally unwanted signal. We see them as an additive noise, of distribution clearly not

Gaussian yet still reasonably similar.

Especially for the case of additive white Gaussian noise a number of techniques using

wavelet-based thresholding have been proposed. In the recent years there has been a fair

amount of research on wavelet thresholding and threshold selection for image denoising [1],

because wavelet provides an appropriate basis for separating noisy signal from the image

signal.

Image denoising techniques are necessary to remove such random additive noises while

retaining as much as possible the important signal features. The main objective of these types

of random noise removal is to suppress the noise while preserving the original image details.

Statistical filters like Average filter, Wiener filter can be used for removing such noises but

the wavelet based denoising techniques proved better results than these filters.

The image f is corrupted by independent and identically distributed zero mean, white

Gaussian noise nij with standard deviation σ i.e. nij ~ N(0, σ2). The goal is to estimate the

signal f from the noisy observations gij=fij+ nij such that the Mean Square Error (MSE) is

minimum. To achieve this the gij is transformed into wavelet domain, which decomposes the

gij into many subbands, which separates the signal into so many frequency bands[5].

Due to the decomposition of an image ,the original image is transformed into four pieces

which is normally labeled as LL, LH, HL and HH as in the schematic depicted in figure 2.2a.

Page 10: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

The LL subband can be further decomposed into four subbands labeled as LL2, LH2, HL2

and HH2 as shown in figure 2.2b.

(a)one level

(b)two level

Figure 2.2 Image decomposition using wavelet transform

The LL piece is the most like original picture and so is called the approximation. The

remaining pieces are called detailed components.

The transform alternately processes the rows and columns of the image by applying high- and

low-pass filters (figure 2.3). These filters separate high- and low-frequency areas of the

image. The result is a smaller version of the original image and the three groups that contain

the highest horizontal, vertical, and diagonal frequencies present in the image. These groups

are referred to as sub-bands. This decomposition process is recursively applied on the smaller

image (figure 2.4).

Page 11: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

Figure 2.3 Wavelet decomposition process[7]

Figure 2.4 Repeated decomposition[7]

The small coefficients in the subbands are dominated by noise, while coefficients with large

absolute value carry more signal information than noise. Replacing noisy coefficients (small

coefficients below certain value) by zero and an inverse wavelet transform may lead to

Page 12: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

reconstruction that has lesser noise. Normally Hard Thresholding and Soft Thresholding

techniques are used for such denoising process.

2.2.5 Conversion from YCbCr to RGB

After the luminance channel has been processed, so to regain colors we convert back the

image the RGB space, and cut out the symmetric extension part of the image to recover the

image with original size.

The conversion formula from YCbCr to RGB are

R = Y + 1.402 (CR - 128)

G = Y – 0.34414 (CB -128) – 0.71414 (CR -128)

B = Y + 1.772 (CB - 128)

2.2.6 Equalizing the color mean

This step enables to suppress the predominant color (blue and green) by equalizing the RGB

channel means. Histogram equalization is implemented on all the three RGB channels. The

intensities are better distributed on the histogram.

Histogram equalization is a technique for adjusting image intensities to enhance contrast.Let f

be a given image represented as a mr by mc matrix of integer pixel intensities ranging from 0

to L − 1. L is the number of possible intensity values, often 256. Let Pn denote the

normalized histogram of f with a bin for each possible intensity. So

Pn = number of pixels with intensity n / total number of pixels

n = 0, 1, ..., L − 1.

The histogram equalized image g will be defined by

gi,j =floor((L-1)

where floor() rounds down to the nearest integer.

Page 13: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

Chapter 3

3.1 Results

A test image(color image) as shown in Figure 3.1 is considered for processing. This image

has low contrast, blue green effect (predominant in blue color ) non uniform lighting and

noise.

The test image was converted from RGB color space to YCbCr color space. This color space

conversion allows to work only on luminance channel(Y) corresponding to intensity

component (gray scale image ).The Y channel shown in fig 3.2 is used for further processing.

The test image considered has low contrast and non uniform illumination. Homomorphic

filtering eliminates all these problems and at the same time it also sharpens the edges of the

image. To make the illumination of the test image more clear high frequency components are

increased and low frequency components are decreased. The output in Figure 3.3 has uniform

illumination and contrast when compared to test image.

Page 14: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

CONCLUSION

In this project, wavelet based image denoising technique is used for removing additive noise

in the underwater images. Before applying the wavelet shrinkage function, first homomorphic

filtering is used to correct non-uniform illumination of light. Homomorphic filter

simultaneously normalizes the brightness across an image and increases contrast. After

correcting non uniform illumination using homomorphic filtering, anisotropic filtering is used

to smooth the image in homogeneous area but preserve image features and enhance them.

Finally, applying wavelet denoising technique to denoise the image. Wavelet based image

denoising techniques are necessary to remove random additive Gaussian noise while

retaining as much as possible the important image features.

The whole of the algorithm used in this project is to be coded in MATLAB. A GUI is to be

developed in MATLAB to showcase the processed image after every step.

Page 15: under water image enhancement

ENHANCEMENT OF UNDER WATER IMAGES

Dept Of E&C, NMAMIT

REFERENCES

[1] David L.Donoho, “De-Noising by Soft-Thresholding,” IEEE Transactions on Information

Theory, vol. 41, no. 3, pp. 613-626, May 1995.

[2] Kashif Iqbal, Rosalina Abdul Salam, Azam Osman and Abdullah Zawawi Talib

”Underwater Image Enhancement Using an Integrated Colour Model”

[3] Pietro Perona and Jitendra Malik, “Scale Space and Edge Detection using Anisotropic

Diffusion,” IEEE transactions on Pattern Analysis and machine Intelligence, vol. 12, No. 7,

pp. 629-639,July 1990.

[4] Stephane Brazeille ,Isabella Quido ,Luc Jaulin, Jean Phillepe Malkasse, ”Automatic

Underwater Image Pre Processing”,2006.

[5] S. Grace Chang,Bin Yu and Martin Vetterli, “Adaptive Wavelet Thresholding for Image

Denoising and Compression,” IEEE Transactions on Image Processing, Vol. 9, No. 9, pp.

1532-1546,september 2000.

[6] “Underwater Image Denoising Using Adaptive Wavelet Subband Thresholding”,IEEE

paper,2010

[7] U Mapen Barry ,”Image Enhancements in the Wavelet Domain”,2008