under water image enhancement
DESCRIPTION
enhances under water imagesTRANSCRIPT
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.
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
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
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.
ENHANCEMENT OF UNDER WATER IMAGES
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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.
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
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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)]
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= 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|)
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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.
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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).
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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
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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.
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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.
ENHANCEMENT OF UNDER WATER IMAGES
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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.
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