COLOR CHANGES IN A NATURAL SCENE DUE TO THE INTERACTION BETWEEN THE LIGHT

AND THE ATMOSPHERE

Colour Imaging LaboratoryDepartment of OpticsUniversity of Granada (SPAIN)

Javier RomeroProfessor

Javier Hernández-AndrésAssociate Professor

Raúl LuzónPh.D. student

Juan L. NievesAssociate Professor

COLOR CHANGES IN A NATURAL SCENE DUE TO THE INTERACTION BETWEEN THE LIGHT AND THE ATMOSPHERE

• Motivation and State of the Art

• Physical model

• Experiment

• Colour changes with distance

• Conclusions and future work

Motivation

distance

size decreases

spatial frequency increases

blur increases

Light is degraded due to its interaction with molecules and particles in the atmosphere.

Degradation depends on the range (distance) and on the wavelength.

Motivation

• Multiple Scattering :

Incident Beam

First Order

Third Order

Second Order

• Single Scattering :

Incident Beam

Size: 0.01 μm Size: 0.1 μm Size: 1 μm

( Mie 1908 )

Motivation

Light is degraded due to its interaction with molecules and particles in the atmosphere.

* reduction in visibility and contrast* color changes:

-less saturated colors,-hue change,

Reversibility?

Motivation

Are color and spectral degradation reversible?

“De-weathering” images?

Color, size, shape, texture are the main features for pattern recognition...

...in addition to spectral information which can influence surveillance and identification.

Clear Day Image

Foggy Day Image

Current image enhancement algorithms

1) Non-physics-based algorithms:• Based on statistical information of the image,• ... using no information about the imaging physics.

2) Physics-based models:• Using the underlying physics of the atmospheric

degradation process...• ...and then to compensate for it with appropriate

image processing.

State of the Art

Histogram equalization and its variations (Pitas and Kiniklis [1996], Pizer et al. [1987]). •RGB channels as separate channels•Certain improvement on HSI space.

Advantages DrawbacksStraightforward technique False colorsNot intensive computation Undesirable effects

Increase the global contrast

State of the Art1) Based on statistical information of the scene:

OriginalHistogram equalized

Light interaction with particles and molecules of different sizes in the atmosphere:

• Absorption-Emission; • Scattering:-Attenuation

-Airlight

State of the Art

McCartney [1976]

2) Physics-based models:

The best physical based models are those constructed over the dichromatic atmospheric scattering model (Tan and Oakley [2001], Narasimhan and Nayar [2000]).

These models are based on single-scattering.

State of the Art

Narasimhan and Nayar (2003)

Assuming the same β for all color channels…

…the color of a scene point is a linear combination of the direction of airlight and the direction of direct transmission (attenuated by scattering)

2) Physics-based models:

Advantages DrawbacksExploit the underlying

physics of the degradation process

Usually needs information about meteorological

conditionsGood color recuperation Some images taken under

different weather conditionsApplicable for different

distancesIdentify some points on the

sceneSimplification of real process

State of the Art2) Physics-based models:

From Tan and Oakley [2001]

Original Enhanced with physical model

RGB HSI

State of the Art

Simple and fast algorithm to recover color information (and spectral information)... for clear days and overcast days.

Only one image: no distance information, and no scattering coefficients values.

But, we need first to analyze and to quantify the color changes due to the atmosphere.

Our goal

• Motivation and State of the Art

• Physical model

• Experiment

• Colour changes with distance

• Conclusions and future work

Physical ModelThe irradiance (E) in one pixel is proportional to the radiance of the scene (L), assuming there is no absorption and reflection inside the camera

For perfect Lambertian surfaces

πλλρλ )()()( d

OEL =

)()( λλ LE Ω=

Physical ModelRadiance from the object at the camera plane has two terms(Narasimhan and Nayar [2000], [2003]):

• one due to direct light coming from the object and attenuated by the atmosphere

• other term: airlight

where: L is the object radiance viewed from the observer planeL0 is the object radianceβtot = βsct + βabs , is the attenuation coefficient in the

atmosphereL∞ is the radiance of the horizond is the distance between the object and the detectorλ is the wavelength

Direct light Airlight

( ) ( )0( ) ( ) ( )(1 )tot totd dL L e L eβ λ β λλ λ λ− −

∞= + −

For clear skies, a Lambertian object receiving an irradiance Ed produces an irradiance on the detector :

Physical Model

( ) ( )( ) ( )( ) ( )(1 )tot totd ddt

EE e L eβ λ β λλ ρ λλ λπ

− −∞= Ω +Ω −

where: Ω is the solid angle subtended from the object into the

detectorEd is the irradiance over the objectρ is the spectral reflectance of the objectβtot is the attenuation coefficientd is the distance between the object and the detectorL∞ is the horizon radianceλ is the wavelength

For overcast skies, assuming an homogeneous distribution of the sky radiance [Gordon and Church [1966]) and a Lambertian object:

( ) ( ) ( ) ( ) ( ) ( )( )1tot totd dtE L e L eβ λ β λλ λ ρ λ λ− −

∞ ∞=Ω +Ω −

Physical Model

where: Ω is the solid angle subtended from the object into the

detectorρ is the spectral reflectance of the objectβtot is the attenuation coefficientd is the distance between the object and the detectorL∞ is the horizon radianceλ is the wavelength

• Motivation and State of the Art

• Physical model

• Experiment

• Colour changes with distance

• Conclusions and future work

Experiment

Color changesCIE 1931 (x,y,Y) and CIELAB (L*,a*,b*) values corresponding to 240 objects of the GretagMacbethColor-Checker DC, whose spectral reflectances are known

GretagMacbethColorChecker DC

SpectraScan PR-650 spectroradiometer

Experiment

Experiment

We know the scattering coefficient at 450, 550 and 700 nm and we can interpolate to the rest of visible spectrum assuming that (McCartney [1976]):

1sct ucteβ

λ=

Another assumption: the absorption coefficient is constant in the visible range.

Experiment

( ) ( )( ) ( )( ) ( )(1 )tot totd ddt

EE e L eβ λ β λλ ρ λλ λπ

− −∞= Ω +Ω −

Day βsct(550 nm) Mm-1 βabs(670 nm) Mm-1 u

15/March/2010 (dust) 50.21 7.83 1.7916/March2010 (clear) 42.06 17.78 1.8919/March/2010 (dust) 100.04 51.08 0.3716/April/2010 (overcast) 80.60 40.95 1.8820/April/2010 (overcast) 62.26 43.66 1.9328/April/2010 (clear) 56.76 65.44 1.59

Experiment

1sct ucteβ

λ=

• Motivation and State of the Art

• Physical model

• Experiment

• Colour changes with distance

• Conclusions and future work

Colour changes in the objectwith observation distance

Six days

240 objects

Distances from 0 to many km

Colour changes in the objectwith observation distance

Colour changes in the objectwith observation distance

Direct light from the object is attenuated

with the distance

For a specific distance, airlight

becomes more important.

Colour changes in the objectwith observation distance

Colour changes in the objectwith observation distance

Colour changes in the objectwith observation distance

20/Apr/2010 Overcast day

Colour changes in the objectwith observation distance

CIELAB

Colour changes in the objectwith observation distance

CIELABAre these colour changes reversible? Are we able to enhance visibility forbetter identification?

…if so, some kind of colour constancycould be achieved.

...and what does “color constancy”mean?

…finding both a color mapping and the color of the sceneilluminant are equivalent problems.

Colour appearance can chage dramatically underdifferent illumination conditions…

CC

T =

2760

KC

CT

= 51

90K

Incandescent lamp

Day-light

…but the human visual system is able tocompensate for those chages.

...and what does “color constancy”mean?

Colour appearance can chage dramatically underdifferent illumination conditions…

What about the images degradated by the atmosphere?

...and what does “color constancy”mean?

Cones excitations changeregularly with illumination

( ) ( )( ) ( )( ) ( )(1 )tot totd ddt

EE e L eβ λ β λλ ρ λλ λπ

− −∞= Ω +Ω −

( ) ( ) ( ) ( ) ( ) ( )( )1tot totd dtE L e L eβ λ β λλ λ ρ λ λ− −

∞ ∞=Ω +Ω −

Clear daysClear days

Overcast daysOvercast days

For a particular object: L viewed under different distances

versus L under the E illuminant (flat spectrum)

Same for M and S cones or for just R, G, B

...and what does “color constancy”mean?

20 objects from the Color Checker

For a zero distance we should expect a linear relation:

L

LE

...and what does “color constancy”mean?

Other distances?Other cones (M or S)?Other broad band sensors (R,G,B)?

20 objects from the Color Checker

For a zero distance we should expect a linear relation:

...and what does “color constancy”mean?

Other distances?Other cones (M or S)?Other broad band sensors (R,G,B)?

It`s clear that visibility of objects depends on weather conditions and changes in the objects’ color can influence identification.

Conclusions and future work

Colour constancy approaches could be applied in bad weather conditions to restore the colour appearance of objects.

?

Javier RomeroProfessor

Javier Hernández-AndrésAssociate Professor

Raúl LuzónPh.D. student

Juan L. NievesAssociate Professor

Thank you for your attention!

References

1. W. E. K. Middleton, “Vision through the atmosphere”, 2nd Edition, University of Toronto Press, 1952 2. I. Pitas and P. Kiniklis, “Multichannel Techniques in Color Image Enhancement and Modeling”, Image Processing, IEEE Transactions, Vol 5,No. 1, pp. 168-171, 1996.3. Stephen M. Pizer, E. Philip Amburn, John D. Austin, Robert Cromartie, Ari Geselowitz, Trey Greer, Bart ter Haar Romeny, John B. Zimmerman and Karel Zuiderveld, “Adaptive histogram equalization and its variations”, Computer Vision, Graphics and Image Processing Vol 39, 355-368, 1987.4. K. Tan and J.P. Oakley, “Physics-Based Approach to Color Image Enhancement in Poor Visibility Conditions”, Journal of the Optical Society of America, Vol. 18, No. 10, pp. 2460-2467, 2001.5. S. G. Narasimhan and S. K. Nayar, “Chromatic Framework for Vision in Bad Weather”, Conference onComputer Vision and Pattern Recognition, IEEE Proceedings. Vol. 1, pp. 598-605, 2000.6. S. G. Narasimhan and S. K. Nayar, “Contrast Restoration of Weather Degraded Images”, Pattern Analysis And Machine Intelligence, IEEE Transactions, Vol. 25, No. 6, pp. 713-724, 2003.7. S. G. Narasimhan and S. K. Nayar, “Vision in Bad Weather”, Seventh IEEE International Conference in Computer Vision, IEEE Proceedings, Vol 1, pp. 820-827, 2000.8. Earl J. McCartney, “Optics of the atmosphere, scattering by molecules and particles”, Wiley-Interscience, 1976.9. Nascimento SMC, Ferreira FP, Foster DH. “Statistic of spatial cone excitation ratios in natural scenes. J Opt Soc Am A ;19:1484–1490 (2002).