28-11-2012, 04:19 PM
Underwater Image Enhancement by Wavelength Compensation and Dehazing
Underwater Image.pdf (Size: 3.44 MB / Downloads: 71)
Abstract
Light scattering and color change are two major
sources of distortion for underwater photography. Light scattering
is caused by light incident on objects reflected and deflected
multiple times by particles present in the water before reaching
the camera. This in turn lowers the visibility and contrast of the
image captured. Color change corresponds to the varying degrees
of attenuation encountered by light traveling in the water with different
wavelengths, rendering ambient underwater environments
dominated by a bluish tone. No existing underwater processing
techniques can handle light scattering and color change distortions
suffered by underwater images, and the possible presence
of artificial lighting simultaneously. This paper proposes a novel
systematic approach to enhance underwater images by a dehazing
algorithm, to compensate the attenuation discrepancy along the
propagation path, and to take the influence of the possible presence
of an artifical light source into consideration. Once the depth map,
i.e., distances between the objects and the camera, is estimated,
the foreground and background within a scene are segmented.
The light intensities of foreground and background are compared
to determine whether an artificial light source is employed during
the image capturing process.
INTRODUCTION
ACQUIRING clear images in underwater environments
is an important issue in ocean engineering [1], [2]. The
quality of underwater images plays a pivotal role in scientific
missions such as monitoring sea life, taking census of populations,
and assessing geological or biological environments.
Capturing images underwater is challenging, mostly due to
haze caused by light that is reflected from a surface and is
deflected and scattered by water particles, and color change due
to varying degrees of light attenuation for different wavelengths
[3]–[5]. Light scattering and color change result in contrast
loss and color deviation in images acquired underwater. For
example, in Fig. 1, the haze in the school of Carangid, the diver,
and the reef at the back is attributed to light scattering, whereas
color change is the reason for the bluish tone appearing in
the brown coral reef at the bottom and the yellow fish in the
upper-right corner.
UNDERWATER IMAGE FORMATION MODEL
The proposed WCID algorithm proceeds in a direction inverse
to the underwater image formation path discussed above,
as depicted in Fig. 4. First, consider the possible presence and
influence of the artificial light source . Next, remove the light
scattering and color change that occurred along the course of
propagation from the object to the camera. Finally, compensate
the disparities of wavelength attenuation for traversing
the water depth to the top of the image and fine-tune the energy
loss by deriving a more precise depth value for every point
within an image.
Fig. 2 illustrates an underwater image formation model. Homogeneous
skylight entering above into the water is the major
source of illumination in an underwater environment. Incident
light traverses from the surface of water reaching the image
scene, covering a range from depth through , where
corresponds to the image depth range. During the course of
propagation, light with different wavelengths is subjected to
varying degrees of attenuation.
CONCLUSION
The WCID algorithm proposed in this paper can effectively
restore image color balance and remove haze. To the best
of our knowledge, no existing techniques can handle light
scattering and color change distortions suffered by underwater
images simultaneously. The experimental results demonstrate
superior haze removing and color balancing capabilities of
the proposed WCID over traditional dehazing and histogram
equalization methods. However, the salinity and the amount of
suspended particles in ocean water vary with time, location,
and season, making accurate measurement of the rate of light
energy loss Nrer difficult. Errors in the rate of light energy
loss will affect the precision of both the water depth and
the underwater propagation distance derived.