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Slide 1
Zhengya Xu, Hong Ren Wu, Xinghuo Yu, Fellow, IEEE, Bin Qiu,
Senior Member, IEEE Colour Image Enhancement by Virtual Histogram
Approach IEEE Transactions on Consumer Electronics, Vol. 56, No. 2,
May 2010
Slide 2
Outline Introduction Previous researches Principle of the
proposed method Experiment
Surveillance videos Varied lighting conditions Few levels of
brightness Histograms have one end is unused while the other end of
the intensity scale is crowded with high frequency peaks.
Slide 5
Previous researches Point-operation-based image enhancement
Contrast stretching Non-linear point transformation improve visual
contrast in some cases, but impairing visual contrast in other
cases Histogram modeling DRSHE, BPDHE, GC-CHE Grey-level to
gray-level Global transform [6] G. Park, H. Cho, and M. Choi, A
Contrast Enhancement Method using Dynamic Range Separate Histogram
Equalization, [7] N. S. P. Kong and H. Ibrahim, Color Image
Enhancement Using Brightness Preserving Dynamic Histogram
Equalization, [8] T. Kim and J. Paik, Adaptive Contrast Enhancement
Using Gain- Controllable Clipped Histogram Equalization,
Slide 6
Previous researches Retinex theory Spatial operations May
enhance the noise or smooth the areas that need to preserve sharp
details Pseudo-colouring Map the grey-scale image to a colour image
Require extensive interactive trials Adaptive histogram
equalization Local enhancement The enhancement kernel is quite
computationally expensive May yield unsatisfactory outputs
Slide 7
Our goals A fast adjustable hybrid approach Controlled by a set
of parameters Take the advantages of point operations and local
information driven enhancement techniques Can enhance
simultaneously the overall contrast and the sharpness of an image
For both colour and luminance components of a colour image
Slide 8
Principle of the proposed method Histogram Used to depict image
statistics in an image interpreted visual format Luminance
histogram and component histogram Provide useful information about
the lighting, contrast, dynamic range, and saturation effects
relative to the individual colour components Must not have very
large spikes in the histogram of the enhanced image Intention of
the proposed method: Find a monotonic pixel brightness
transformation q=T(p) for a colour image Meet specific requirements
Be as uniform as possible over the whole output brightness
scale
Slide 9
Definitions and notations Pixel coordinates of a colour image
M:height N: width of the image The pixel in RGB (Red, Green, Blue)
colour space The pixel in colour space
Slide 10
Definitions and notations For each RGB colour channel, each
individual histogram entry cardinality function,, :scale of
component, usually256 The luminance channel histogram of an image
The cumulative histogram from grey-scale image
Slide 11
Local geometric information An enhanced image with good
contrast will have a higher intensity of the edges Laplacian
operator : Applied to each of the RGB channels The sum of absolute
value of the pixel processed with a Laplace operator
Slide 12
Principle of the proposed method Define w default :2, v default
:1 is designed to suit special enhancement requirements for the
image interpretation Using a normalization coefficient
Slide 13
The output histogram can be approximated with (16) by its
corresponding continuous probability density : M and N are the
height and the width, and the output brightness range is The
desired pixel brightness histogram transformation T is defined as :
Principle of the proposed method
Slide 14
The quantisation step-size is obtained as follows : The second
term is used to enhance contrast for a specified range, The third
term is dependent on the image structure. Parameter v can be
adjusted In most cases, v is fixed as 1, since the enhanced result
is not very sensitive to the change of the v
Slide 15
Principle of the proposed method The quantisation step-size is
obtained as follows : Human vision is very sensitive to the
interval value The default values of these parameters are:
Slide 16
Principle of the proposed method The number of reconstruction
levels of the enhanced image must be less than or equal to the
number of levels of original image. When the contrast of a dark
area whose histogram spans a broad range of the display scale is
enhanced, the bright areas may be out of the display range.
Therefore, a hard-limit is needed
Slide 17
A hard-limit Using parameter t 222 smooth the enhancement
contrast over the full brightness scale 111 44 1 is actually
similar to t for human vision The ratio (Weber fraction), is nearly
constant at a value of about 0.02 The default value of t is set to
3 If an image with its histogram basically concentrated in a very
bright region, the image can be inversed [2] William K. Pratt,
Digital Image Processing, John Wiley & Sons, 2008.
Slide 18
Transformed back to the RGB colour space After the contrast
enhancement in the luminance channel, the output image is
transformed back to the RGB space A histogram of RGB channels may
be saturated at one or both ends of the dynamic range The linear
mapping of video signal from the RGB colour space to the YCC colour
space where the luminosity (Y) is a function of R, G and B which
are normalized to 1, and denoted as Y(R,G,B)
Slide 19
Transformed back to the RGB colour space We only need to find
the corresponding Y values to the upper and the lower bounds of the
RGB channels The conversion from the RGB space to the YC B C R
space is the number of the saturated pixels in the image
Slide 20
Transformed back to the RGB colour space Compared with four
classical enhancement methods linear contrast stretching contrast
reverse gamma correction histogram equalization Recent developed
histogram equalization based methods DRSHE BPDHE GC-CHE The test
images include well-known typical test images including
Mountain,Scene, Meat etc Image size: 500x362 or 721X481 or
768X768or 731X487
Slide 21
Result a)original image b)output of the proposed approach-1, c)
output of proposed approach-2 d) output of proposed approach-3 e)
output of modified linear stretching f) output of histogram
equalization.
Slide 22
Result a) original image, b) output of the proposed approach,
c) output of histogram equalization, d) output of linear
stretching, e) output of contrast reverser, f) output of modified
linear stretching.
Slide 23
Result a) original image, b) output of the proposed approach,
c) output of histogram equalization, d) output of linear
stretching, e) output of gamma correction, f) output of GC-CHE
Slide 24
Result a)original image, b) output of the proposed approach, c)
output of histogram equalization, d) output of gamma correction, e)
output of PBDHE, f) output of DRSHE
Slide 25
Conclusion Based on modification of a virtual histogram
distribution Information extracted from salient local features A
new way to integrate colour and brightness information extracted
from salient local features, for global contrast enhancement.
Output value scaling bounds control