Retinex Theory for Image Enhancement
Jaya Shrivastava*
and G.S. Verma
Rungta College of Engineering and
Technology, Bhilai. Chhattisgarh (India).
ABSTRACT:
Multi-scale
retinex (MSR) processing has been shown to be an
effective way to enhance image contrast, but it often has an undesirable desaturating effect on the image colors. A
color-restoration method can help mitigate this effect, but our experience is
that it simply leads to other problems. In this paper we modify MSR so that it
preserves color fidelity while still enhancing contrast. We then add neural-net
based color constancy processing to this modified version
of MSR. The result is a principled approach that provides the contrast
enhancement.
INTRODUCTION:
MSR as a method of image enhancement which provide
color constancy and dynamic range compression. Nonetheless, there are a number of problems with the
original MSR method. The chief conceptual problem is that a number of
image-processing tasks are performed simultaneously.
MSR
serves a subset of the following five image processing goals, depending on the
circumstances:
1)
Compensating for uncalibrated devices (gamma
correction)
2)
Color constancy processing
3)
Local dynamic range compression
4)
Global dynamic range compression
5)
Color enhancement
In
the original MSR method all the processing steps are intertwined, and as a
result, the colors are changed in image dependent and unpredictable ways.
Basic Approach
The general mathematical
formulation of the center /surround Retinex is
Where 
denotes the Retinex
output Ii(x,y)
the image distribution in the ith
spectral band, “
” the convolution operation ,and F(x,y)
the surround function
Where c is the Gaussian
surround space constant and K is selected such that
The MSR output is then simply a weighted sum of the
outputs of several different SSR output.
Where, N is the number of scales, 
the ith component of the n’th scale, 
the
ith spectral component of the MSR output,
and wn the weight associated with the n’th scale.
The only difference between 
and 
is
that the surround function is now given by
The color restoration method for the MSR is given by
Where
is
a constant parameter of the color restoration function.
The MSRCR is given by:
The final version of MSRCR can be written as:
MSR and Color
fidelity
Each operation the above sequence changes the image
colors. The logarithm in applied to each of the channels independently creates
a color shift. The differencing step in moves the image colors towards grey.
Finally, the color-restoration step multiplies the result by the logarithm of
the original color[3], which changes the color in a
way which is hard to characterize. More specifically, the restoration effect is
a non-linear function of the original image color and the processed image
color, itself a function of the original image. The amount of color added with
this scheme can at best only approximate the color removed in the first step;
this confounds any color constancy processing that may have been intended[4].
Of course, it may be the case that the color balance of the input image is
incorrect, and should be changed. This occurs when there is a mismatch between
the illumination for which the imaging system is calibrated and the actual
scene illumination. In this case, color constancy processing is required, and
our approach is to apply a sophisticated color constancy algorithm to the image
to estimate the proper image .
RESULT:
We have tested our modified MSR method on a number of
images; however, we cannot reproduce them in color in these proceedings. Rather
than attempt to portray color results in black and white, we have made the results
of a controlled sequence of images available on the Internet In that sequence we took images of the same
scene with a shadow of varying strengths using two very differently colored
lights. We have tested the modified method of MSR processing on a number of
images. Rather than attempt to portray In the case of the modified algorithm,
the color constancy processing using the method describe in works well, producing an image close to the
desired color, as set by the standard image. The subsequent MSR processing
preserves this color, producing an image which has the benefits of the MSR
dynamic range compression, and is the desired color.
CONCLUSION:
Standard Multi-scale retinex
processing works quite well as a method of compressing an image's dynamic rangeso that the image contrast
looks better. Standard MSRperforms
a mixture of local (via ratios) and global (via logarithms) contrast
adjustment. Unfortunately, standard MSR has the drawback that it
perturbs the image colors in quite unpredictable ways. We have analyzed the
fundamental steps of MSR and disentangled the various operations so that their
effects can be handled separately, which also makes it possible to add in true
color constancy processing as one of the steps. The resulting algorithm provides
better color fidelity, has fewer parameters to specify. In addition, it is less
computationally expensive.
REFERENCES:
1.
Zia-ur Rahman, "Properties of a
Center/Surround Retinex Part One: Signal Processing Design,"
NASA Contractor Report #198195, 1995.
2.
Zia-ur Rahman, Daniel J. Jobson, and
Glenn A. Woodell, "A Multiscale
Retinex for Colour Rendition
and Dynamic Range Compression," SPIE International Symposium on Optical Science,
Engineering and Instrumentation, Applications of Digital Image Processing XIX, Proceedings
SPIE 2825, Andrew G. Tescher, ed., 1996.
3.
Daniel J. Jobson,
Zia-ur Rahman, and Glenn A.
Woodell, "Retinex
Image Processing: Improved Fidelity To Direct Visual
Observation," Proceedings of the ISandT/SID
Fourth Color Imaging Conference: Color Science, Systems and Applications,
Scottsdale, Arizona, November, pp. 124-126, 1996.
4.
E. H. Land,
“Recent advances in Retinex theory and some
implications for cortical computations: Color vision and the natural image,”
Proc. Natl. Acad. Sci., 80, pp. 5163-5169, 1983.
5.
E. H. Land,
“Recent advances in Retinex theory,” Vision Res., 26,
pp. 7-21, 1986.
6.
Edwin H. Land, “An
alternative technique for the computation of the designator in the Retinex theory of color vision,” Proc. Natl. Acad. Sci.
USA, Vol. 83, pp. 3078-3080
7.
Funt, B., Cardei, V. and Barnard,
K., “Learning Colour Constancy,” Proc. Fourth ISandT/SID Color Imaging Conf., pp. 58-60, Scottsdale, Nov.
19-22, 1996.