Presented by

Pooja G Bidwai

Sources of underwater image distortion are

1. Light Scattering

2. Color Change

Light scattering lowers the visibility and contrast of

captured image.

Color change leads to the varying degrees of

attenuation.

Fig.1 Hazing and bluish effect caused by light scattering and color change

in underwater images.

To enhance the underwater image Wavelength

compensation and dehazing algorithm is most

appropriate(WCID).

Enhanced visibility and superior color fidelity can be

obtained by WCID algorithm.

Image dehazing helps to restore clarity of underwater

images.

Haze is caused by suspended particles such as sand,

minerals in lakes, river and oceans.

Capturing image underwater is challenging due to haze

caused by light.

Using hazy image formation model, image

formed at camera can be given as:

X= point in underwater scene,

= image captured by camera

= scene radiance at point x.

= residual energy ratio.

= homogeneous background light

Residual energy ration can be given as:

Normalized residual energy ratio depends on light

wavelength transmitted & it can be shown as:

Different wavelengths of light are attenuated at diff rates in water

Incident light traverses from surface of water reaching

the image scene covering range from D through D+R,

where R corresponds to image depth range.

Amount of residual light w(x) formed after wavelength

attenuation can be:

(3)

Light J(x) emanated from point x is equal to the amount

of illuminating ambient light Reflected i.e.

Is the reflectivity of point x

(4)

Eq. 1 can be modified as :

(5)

This equation incorporates light scattering during

propagation from object to camera d(x), and

wavelength attenuation along both D(x) and d(x).

Haze increases with distance; so, haze can be useful to

determine d(x).

Using single image haze removal and dark channel

prior d(x) can be determined-

If point x belongs to a part of foreground then

Jdark(x) ->0

But background light is usually assumed to be pixel

intensity with highest brightness,

Thus foreground and background intensities are known

and further it is used to segment background and

foreground.

Depth map of fig.1obtained by estimating d(x), i.e. distance between object

and the camera using dark channel prior

Artificial lights are often supplemented to avoid

insufficient lighting in underwater photographic

environment.

Artificial light source can be detected by comparing

luminance difference of foreground and the

background.

Based on depth map derived:

◦ Area = foreground if d(x)> σ

◦ Area = background if d(x)<= σ

Influence of artificial lighting is a function of amount

of luminance by light source and surface reflectance of

objects.

Illuminated by artificial light source, intensity of foreground appears

Brighter than that of background.

After detecting the artificial light source, can be

removed by subtraction from main image formation

equation:

After removal of artificial light in fig 1. is shown in right panel

Of split screen, whereas original one is in left panel.

Haze can be removed by subtracting term (1-tλ(x)).Bλ

in eq.1:

Image obtained after eliminating haze, light scattering and color change .

Image obtained after processing with (a)WCID (b) dark-channel based Dehazing (c) chromatism-based dehazing (d) histogram qualization

RESULT

WCID results in superior haze removal and color

balancing capabilities over dehazing and histogram

equalization.

Highest SNR values are obtained.

Performance of WCID is most robust through different

water depths.

John Y.Chinag and Ying-Ching Chen, “Underwater image enhancement by

wavelength compensation and dehazing,”IEEE transaction on image processing,

vol.21,no.4,April 2012.

K. He, J. Sun, and X. Tang, “Single image haze removal using DarkChannel Prior,”

in Proc. IEEE CVPR, vol. 33, no.12,Dec.2011.

K. Lebart, C. Smith, E. Trucco, and D. M. Lane, “Automatic indexing of

underwater survey video: algorithm and benchmarking method,” IEEE J. Ocean.

Eng., vol. 28, no. 4, pp. 673–686, Oct. 2003.

Y. Y. Schechner and N. Karpel, “Recovery of underwater visibility and structure by

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