A REPORT ON COLOR IMAGING

TECHNIQUES USED ON SOME NON-

CANONICAL IMAGES

LAB 2

[COLOR AND MULTISPECTRAL IMAGING…] The objective of this lab was to study and understand the efficiency of some of the color consistency algorithms.

Report Prepared By-Sharib ALI

Submitted to: Prof. Alamin MANSOURI

Abstract: Different images with different illuminace were provided and asked to apply some useful

algorithms to get the better quality of images. Some algorithms were better in some while

the other were better in the other. Like for example when we took Grey World for some

darker images we got good contrast image but the effect could hardly be distinguisible in

the images which were fairly bright.

The abstract of my work is that i tried to achieve the best quality image under different

algorithms and justify my results with the observations and MSE calculations. However,

time calculations were also one of the key element to judge the efficiency of the algorithms

I have used.

Color Consistency: The digital camera records large shifts in image colors under the different illuminations.

However, a human observer viewing each scene will be able to discount the color of the

illumination and perceive the colors in each scene as the same. This property of

compensating for illumination is called color constancy.

Illumination is rarely constant in intensity or color throughout a scene. Multiple light

sources with difference spectra. Almost all the color consistency algorithms assumes that

the spectrum of the spectrum of the incident illumination remains constant across the

scene.

1. GrayWorld

The grey world algorithm assumes that the average of all colors in an image is grey,

i.e. the red, green and blue components of the average color are equal. The amount

the image average departs from grey determines the illuminant RGB. This

assumption is held very well: in real world image, it is usually true that there are a

lot of different color variations. The variations in color are random and

independent; the average would converge to the mean value gray given an amount

of samples. Color balancing algorithms can apply this assumption by forcing its

images to have a common average gray value for its R,G and B components. In case

the image is taken by the digital camera under a particular lighting environment, the

effect of the special lighting cast can be removed by enforcing the gray world

assumption on the image. As a result the approximation, the color of the image is

much closer to the original scene.

Algorithm:

We find the mean of RGB channel

We find the mean value of each channel R, G and B

Kr=avg/R;Kg=avg/G;Kb=avg/B

Finally we get the new channels as:

B

G

R

K

K

K

B

G

R

B

G

R

c

c

00

00

00

Where, K values represents the coefficients of reflectance

2. White Patch

The white patch algorithm presumes that in every image there will be some surface

or surfaces such that there will be a point or points of maximal reflectance for each

of the R, G, and B bands. This algorithm realizes on having a bright patch at

somewhere in the image. The idea is that, this patch reflects the maximum intensity

of light possible for each band. Assuming that we are dealing with the approximate

Lambertian reflection, this will be the color of the illuminant. Once a bright patch

has been located, its color value can be used for an estimate of the white point.

Algorithm:

We find the maximum values for each channel in RGB space

We take the S(surface reflectance) to be 255

Using formula- Kr=S/Rmax and similarly for Kg and Kb

We finally apply the matrix above to get the new values for R,G and B

3. Modified White Patch Algorithm The white patch algorithm does not work when there is presence of noise or when

the maximum values equals to 255 in the image as it would make no change to the

calculated coefficients.

So, we apply the threshold to the RGB space and estimate the illumination from

bright zones that having the mean value. The color bands are re-scaled using this

white point estimation.

Algorithm

We take the mean of each channel

We take the maximum value of each channel and compare it with the

threshold.

If its true then we apply the illuminant coefficient to that channel in

particular

If the condition is false we don’t do any thing to that channel

4. Progressive Combination of gray World and Modified White Patch As the name implies progressive combination, it combines GW and MWP to give

better quality images. It trades of between all the ranges of the image. For the pixel

values below a threshold h1 (say) corresponds to the GW and the pixel values of the

image above certain threshold say h2 corresponds to the MWP. So, we apply GW

algorithm and MWP algorithms to certain pixel values. The pixels between them are

also updated with some reflectance coefficients which is given by some empirical

formula given below.

Step I.

Find the Average intensity ‘I’.

3

BGRI

Step II.

Comparing the average intensity with the thresholds (h2=20 threshold for GW and

h1=120(MWP)

Case 1: I<=h2

We do the calculations for the Gray world as shown in the algorithm above in GW

part.

Case 2: I>=h1

We do the calculations for the MWP as shown in the algorithm of MWP

Case 3: in between

21

2

21

1

hh

h

hhI

Calculate the value of k for each channel with the formula below. Apply reflectance

coefficients for the GW and MWP with certain factors as given below:

MWPGWk )1(

5. Retinex The main idea of Retinex was conceived by Edwin Land as a model of the lightness and color perception of human vision. The Retinex is a general purpose image enhancement algorithm that is used to improve the contrast, brightness and perceived sharpness of images primarily through dynamic range compression. It synthesizes contrast enhancement and color constancy by performing a non-linear spatial/spectral transform that mimics traits seen in the human vision system.

SSR

Algorithm for Single Scale Retinex

We take a Gaussian mask, given the value of ‘c’, standard deviation of the same size as that of the image to be reconstructed

We take the convolution between this mask and the original image

Io= yxIyxF i ,),(

Finally, we take the logarithmic difference between the original image and the convoluted output

SSR= log[),(log yxI i yxIyxF i ,),( ]

MSR-CC

The real need for MSRCC is to get the good image with the combined SSR images

obtained with different Gaussian masks having different values of c (we have taken

50,70,25 ,100) with different weights.

Algorithm for MSR-CC (Multiscale Retinex with Color Correction)

1. Calculate the SSR for different values of c

2. Sum all the results by giving some weight to each of your result

3. Finally, for the color correction

1

),(log),(log),(i

iii yxIyxIyxC where, is a critical parameter to

tune manually

4. We can multiply the final result with certain gain G and offset to improve our

result.

ACE (Automatic Color Correction)

ACE aims to model a simplified version of the inner complex behavior of the human visual system. It has been devised primarily for the enhancement of digital images, characterized by a lower dynamic range compared to the high range of real world scenes. To differentiate the real process from its model, we use the word adaptation to indicate the biological adaptations made by the human visual system in the real world and the word adjustment to indicate the simplified adaptations of ACE in the digital images domain. Algorithm

1. Chromatic Spatial Adjustment: It is done for the color constancy and contrast tuning. A spatial Comparison is done weighted by pixel distance

),(

)()()(,

jpdjIpIrpR

pjimgj

cc

2. Linear adjustment of the intensity of the output Rc is done within the interval (o, 255). It configures the output range to implement an accurate tone mapping, performing lightness constancy.

Fig. Showing different algorithms applied

PARAMETERS Used (in Above Image Algorithms)

Progressive Combination

h1=120 MWP h2=20 GW

Single Scale Retinex for C=5

Multiscale Retinex With Color Correction (MSRCC)

c w 25 0.1 35 0.25

alpha=60 45 0.5 offset=0, gain=1 100 0.7

Automatic Color Equalization(ACE)

r(.)=75

slope=255-0/max-min

CALCULATING MSE (Mean Square Error) and delE (Euclidean distance)

Mean Square Error

MSE

nm

IrIm

x

n

y

yxyxo

1 1

2

),(),(

Computing MSE globally

We take the original input image taken under canonical illumination and the result

of output image constructed applying different color consistency algorithms. We calculate

the MSE as given in the formula above. The significance of this error will give the

approximation of the nearness of the output image (taken under non-canonical) using a

certain algorithm and hence gives us the clear idea about its efficiency.

Computing MSE Locally

We define certain Region of interest in our image and take that as the mask to

compute the MSE. The ROI could be the over an area or an edge.

MATLAB: function roipoly (it generates a mask around the image). We have created a

function ROI which will take the mask over the image the user wants to. So, both for over

an area containing an edge and within a uniform area were done .

Euclidean Distance The Euclidean distance between the pixel values is calculated to see the convergence of the

images we get with the different methods of algorithm applied to the original image. First

the images are taken to LAB color space using MATLAB commands like srgb2lab ,

MEASUREMENTS

fish.tif (350x256) MSE GLOBAL MSE LOCAL delE GLOBAL delE LOCAL

Algorithm Time R G B R G B R G B R G B

GW 0.97841 1867.47 3634.09 3758.82 855.25 1659.2 1829.8 605.49 796.9 1652 30.62 175.9 900.3

WP 0.08108 2003.14 259.145 1303.83 915.1 77.048 635.31 605.49 796.9 1652 30.62 175.9 900.3

MWP 0.05043 8399.57 14879.8 8584.95 3394.6 5772.1 3737.6 605.49 796.9 1652 30.62 175.9 900.3

Prog. 0.23951 510.187 6669.22 2658.18 137.58 2824.8 1065.9 605.49 796.9 1652 30.62 175.9 900.3

SSR 12.0831 17588.1 21810.1 21847.2 7745.5 9923.3 10384 6368.3 2957.3 2118 3595 1762 1611

MSRCC 400.987 16284.9 20355.7 21859.7 7373.6 9497.2 10385 8339.3 8781.3 6629 4255 6649 4831

AEC 3709.29 1487.6 1936.72 1352.79 557.23 734.18 542.36 723.48 1411.6 2102 30.64 176.5 942.2

Building.jpg (350x256) MSE GLOBAL MSE LOCAL delE GLOBAL delE LOCAL

Algorithm Time R G B R G B R G B R G B

GW 0.53429 9713.27 10423.2 10012.1 5243.3 5312.3 4852.9 340.99 1839.5 956.1 137.9 645.6 705.4

WP 0.07985 257.42 128.244 102.025 132.69 61.655 42.591 261.21 1386.4 985.2 137.9 540.5 762.3

MWP 0.04496 9178.24 6727.4 8578.76 5022.9 3220.7 3866.2 261.21 1386.4 985.2 137.9 540.5 762.3

Progressive 0.189 6316.24 7919 7446.74 3408 4033.3 3604.5 275.14 1481.8 974.5 137.8 660 757

SSR 3.24469 16396.4 17072.6 16515.4 8855.6 8713.4 8020.6 7011.7 3131.8 3394 4608 1808 2001

MSRCC 235.161 15065.4 15711 16533.3 8304.4 8223.8 8026.4 10140 10760 8620 7627 8674 7157

AEC 1200.32 376.28 361.88 458.57

350.4 209.9 115.4 450 345 223 2234 2334 1234

table.png(350x256) MSE GLOBAL MSE LOCAL delE GLOBAL delE LOCAL

Algorithm Time R G B R G B R G B R G B

GW 0.53429 9713.27 10423.2 10012.1 5243.3 5312.3 4852.9 340.99 1839.5 956.1 137.9 645.6 705.4

WP 0.07985 257.42 128.244 102.025 132.69 61.655 42.591 261.21 1386.4 985.2 137.9 540.5 762.3

MWP 0.04496 9178.24 6727.4 8578.76 5022.9 3220.7 3866.2 261.21 1386.4 985.2 137.9 540.5 762.3

PROG 0.189 6316.24 7919 7446.74 3408 4033.3 3604.5 275.14 1481.8 974.5 137.8 660 757

SSR 3.24469 16396.4 17072.6 16515.4 8855.6 8713.4 8020.6 7011.7 3131.8 3394 4608 1808 2001

MSRCC 235.161 15065.4 15711 16533.3 8304.4 8223.8 8026.4 10140 10760 8620 7627 8674 7157

Some Key Observations: 1. Gray World showed some good result with almost all the images.

2. White Patch did not show better result than the Gray world. The MSE eror of White

patch therefore is seen to be more than the GW in the darker images.

3. Modified White Patch has relatively more MSE and del E. So, it is also not a good

algorithm when there is less noise.

4. Progressive also gave good results for some brighter images. Its better than the

MWP.

5. SSR gave good results with higher values of standard deviation ‘c’.

6. MSRRCC was nearly very good but still there has to be lot to be done with the

contrast and the brightness. However, in context of color it gave some good result.

From the chart it can be seen that MSE is relatively low for MSRCC than SSR

7. One of the main effects of an equalization algorithm(ACE) is the image’s mean

lightness adjustment. Retinex, like most of the color correction algorithms that

search for the white, tends to increase the overall lightness of the image if its mean

lightness is below the medium gray, but does not change it significantly if its mean is

over the medium value. On the contrary, ACE, due to its gray world behavior

component, is able to modify the image’s mean lightness in both directions,

according to its original value. This difference has been eliminated.

8. AEC gives best results for the darker images and fairly good with the brighter

images as it combines both GW and WP for the scaling reference.

Fig. Automatic Color Equalization Underwater

(original image under non-canonical illuminant is undergone ACE)

Fig . Results of some other Algorithms applied to Non-Cannonical Image

Fig. Showing Different Algorithms applied to the Original (non-canonical)

image