Image Enhancement via Dominant Brightness Analysis
1. AN EFFICIENT IMAGE ENHANCEMENT USING DOMINANT
BRIGHTNESS LEVELANALYSIS AND MORPHOLOGICAL
EDGE FILTERING
2. Abstract
• The project presents satellite image contrast enhancement based
on Lifting wavelet based multi scale decomposition and
morphological edge preserving filtering approach.
• Here the system processes the input true color image in separation
of individual planes to adjust contrast.
• This combination will be implemented to increase the visual
perception of satellite color images.
• The low frequency will be enhanced with dominant brightness
level analysis and High frequency band coefficients are enhanced
with top hat filtering model.
• All enhanced frequency subbands are reconstructed with inverse
wavelet decomposition. Finally the reconstructed images will be
post processed with non flat ball shaped structuring element and
morphological erosion process.
3. Objective
• To improve the quality of low contrast satellite image
using Lifting wavelet based multi scale decomposition
dominant brightness level analysis, soft thresholding and
morphological edge preserving top-hat filtering approach.
5. Drawbacks
• Low accuracy in image quality
• It degrades sharpening details due to image border
artifacts
• Less preservation of image content during enhancement
6. Proposed Method
Satellite image contrast enhancement for vision system
based on,
• Lifting wavelet based multi resolution analysis, adaptive
intensity transformation method and Morphological Edge
preserving top- hat filtering
7. Methodologies
• Multi scale Decomposition with Lifting wavelet filter
• Dominant Brightness level analysis
• Intensity transfer function
• Wavelet Soft thresholding
• Edge Preserving Top hat Filtering
8. Block Diagram
Low Contrast
image
Plane
Separation
HF bands
Histogram
Equalization
Multi Scale
Decomposition
Adaptive intensity
transfer function
Soft
thresholding
LF band
Plane
Concatenation
Quality
Evaluation
Image
Reconstruction
Top hat
Filtering
11. • LWT has been employed in order to preserve the high-
frequency components of the image. LWT separates the
image into different subband images, namely, LL, LH, HL,
and HH.
• Low frequency subband contains overall brightness of an
input and high-frequency subband contains the edge
information of input image.
• To avoid problems with floating point precision of the
wavelet filters, Lifting scheme based DWT will be used.
• Lifting scheme is used to map the wavelet coefficients to
integer coefficients and here Daubechies type wavelet filter
is used.
Lifting Wavelet Transformation
13. Forward Lifting in IWT
Step1: Column wise processing to get H and L
H = (Co-Ce) and L = (Ce+ [H/2])
Where Co and Ce is the odd column and even column wise pixel values
Step 2: Row wise processing to get LL,LH,HL and HH,
Separate odd and even rows of H and L,
Namely, Hodd – odd row of H, Lodd- odd row of L
Heven- even row of H, Leven- even row of L
LH = Lodd-Leven ,LL = Leven + [LH / 2]
HH = Hodd – Heven ,HL = Heven + [HH / 2]
Reverse Lifting scheme in IWT
Inverse Integer wavelet transform is formed by Reverse lifting
scheme. Procedure is similar to the forward lifting scheme.
Continues…
14. LWT Sub-band Structure
LL: Horizontal Low pass
& Vertical Low pass
LH: Horizontal Low pass
& Vertical High pass
HL: Horizontal High pass
& Vertical Low pass
HH: Horizontal High pass
& Vertical High pass
16. • To reduce image distortion and saturation artifacts and
preserving the image quality, dominant brightness levels
are analyzed from image luminance subband.
• This will be performed for low frequency component image
and it is decomposed into three sub layers.
• These are low, middle and high intensity layer based on
brightness region in the luminance image.
• The dominant brightness level for this subband will be
determined by the following expression,
D(x, y) = {log L(x, y) + ε}
Where, L(x, y) - Pixel intensity at (x, y) and ε - Small constant
factor that prevents the log function from diverging to infinity.
Brightness level analysis
17. LL Enhancement
Low frequency
subband coefficients
Find Dominant
brightness
Decompose low,
middle, high
intensity layers
Find adaptive
transfer function
Enhanced LL band
19. • These transfer function will be determined by using knee
transfer function and gamma adjustment function for
enhancing the three intensity layers.
• The knee points for low, middle and high intensity layers
are,
Pl = bl + wl(bl – ml) , Ph = bh - wh(bh – mh) .
Where, Pl - Low bound , Ph - high bound, wm - Tuning parameter
and mm , ml , mh – Mean brightness in three intensity layers.
• Gamma adjustment function will be,
Gk(L) = { (L/Mk)1/γ - ( 1 - L/Mk)1/γ + 1}
Where, Mk = Size of each intensity range and Ml = bl ,
Mm = bh
- bl, Mh = 1 – bh
Adaptive Intensity Transfer Function
20. Restoration for High Frequency band
• Wavelet generated high frequency subbands are restored by soft
thresholding method. Here the threshold will be selected for
shrinking high frequency subband coefficients to remove the
noise.
• The soft threshold will be determined by level dependent
method,
Th= sqrt (2.*sigmahat.^2 * L)
Where,
L = Number of coefficients.
sigma = median(C)./0.6745
Where, C - Coefficient Matrix,
21. Continues…
• The soft thresholding is defined by,
Coeff ’ = sign(Coeff) * (Coeff – T) if Coeff > T
= 0 if Coeff < = T
• This Process is applied for all high frequency coefficients
obtained from wavelet decomposition.
• These restored high frequency subbands are further post
processed with morphological top hat filtering to smooth
the detailed components.
22. Top-hat filtering
High frequency
bands
Apply top-hat filter
Top-hat filter: Input –
opening of Input
Edge Enhanced
bands
Morphological opening:
Erosion followed by
dilation
• It is used here to enhance the details present in the
high frequency band and it sharpening the edges and
textures
23. Continues…
• Top-hat filtering is a type of morphological Edge
sharpening filter is used for High frequency subbands.
• It processes the image based on shapes and here ‘line’
structuring elements are used for defining the shapes.
• Top hat filtering requires an morphological opening
operation and opening is combination of dilation and
Erosion.
• This filtering will be applied in all three directions such as
horizontal, vertical and diagonal Edges Details.
• Dilation: It is the process of adding a pixel at object
boundary based on structuring element.
• Erosion: It is to remove the pixel from the object boundary
depends on structuring element.
24. Continues…
Dilation (D) HF (bit Xor) Se
Erosion (E) HF (bit Xnor) Se
Opening (O) E (bit Xor) Se
Top-hat Filter HF – O (HF)
I – Input Image, Se = Line Structuring Element,
HF – High Frequency bands
26. Performance metrics
The performance of system will be evaluated with following
metrics,
• Measure of Enhancement(EME):
EME = (1/(M*N))*[(Imax/(Imin + C))*log(Imax/(Imin + C))]
Where,
• M,N represents the total number of elements in an image,
• Imax represents the maximum Intensity Value,
• Imin represents the minimum Intensity value
• C represents a small constant to avoid dividing by zero.
Here C = 0.0001
27. Continues…
Gaussian distribution Curve: The parameter μ in this
definition is the mean or expectation of the distribution (and also
its median and mode). The parameter σ is its standard deviation;
its variance is therefore σ 2. A random variable with a Gaussian
distribution is said to be normally distributed and is called
a normal deviate.
It is used to illustrate how much changes occurred in the
illumination from low contrast input to enhanced image due to
this enhancement Process.
29. Advantages
• Better accuracy interms of edge preservation
• Flexible and highly compatible method
• It provide an optimal results for low contrast images from
satellite and digital camera
32. Conclusion
The project presented the contrast enhancement approach based
on dominant brightness level analysis and adaptive intensity
transformation for remote sensing images. This algorithm
computed brightness-adaptive intensity transfer functions using
the low-frequency luminance component in the wavelet domain
and transforms intensity values according to the transfer function
gamma adjustment function based on the dominant brightness
level of each layer. High frequency subbands are processed with
shrinkage rule soft thresholding to reduce the impact of noises
and sharpened with morphological top-hat filtering by preserving
Edges. This method proved that an enhance the low quality
images with less image distortion and preserves the edge details.
The system performance will be measured through parameters
such as measure of enhancement and Gaussian distribution
function.
33. References
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Gaussian-Based Edge Enhancement Filters Using Fourier
Analysis”, IEEE Transactions on Acoustics, Speech, and Signal
Processing, (1993), Vol. 5, pp. 13-16.
[2] Day-Fann Shen, Chui-Wen Chiu and Pon-Jay Huang, “Modified
Laplacian Filter and Intensity Correction Technique for Image
Resolution Enhancement”, IEEE International Conference on
Multimedia and Expo, (2006), Vol. 7, Nos. 9-12, pp. 457-460.
[3] Cheevasuvit F, Dejhan K and Somboonkaew A “Edge
Enhancement Using Transform of Subtracted Smoothing Image”,
ACRS, (1992), Vol. 3, No. 12, pp. 23-28
[4] Jin Jesse S “An Adaptive Algorithm for Edge Detection”,
MVA’SO IAPR Workshop on Machine Vision Applications, (1990),
Vol. 9, November 28-30, pp. 14-17.
