14 Underwater Multimodal Survey: Merging Optical and Acoustic Data
223
The ability to measure and model large underwater sites in a short time opens up
many scientific challenges such as image processing, multimodal adjustment, land
visualization and offers new opening to marine biology, underwater archaeology and
underwater industry (offshore, harbour industry, etc.).
14.1.1 Underwater Image Processing: State of Art
The underwater image pre-processing can be addressed from two different points of
view: image restoration techniques or image enhancement methods.
Fan et al. proposed a restoration method based on blind deconvolution and the
theory of Wells (Fan et al. 2010). As a first step an arithmetic mean filter is used to
perform image denoising, and then an iterative blind deconvolution using the filtered
image is carried out. The calculation of the PSF of water is done using the following
equations:
b = cω
(14.1)
H medium (ψ, R) = exp
−cR + bR
1 − exp(−2π θ0 ψ)
2π θ0 ψ
(14.2)
Where θ 0 is referred to the median scattering angle, ψ is the spatial frequency in
cycles per radian, R is distance between sensor and object, b scattering coefficient,
c attenuation coefficient and albedo ω.
Image restoration techniques need some parameters such as attenuation coefficients, scattering coefficients and depth estimation of the object in a scene. For this
reason in our works, the preprocessing of underwater image is devoted to image
enhancement methods, which do not require a priori knowledge of the environment.
Bazeille et al. (Bazeille et al. 2006) proposed an algorithm to enhance underwater
image, this algorithm is automatic and requires no parameter adjustment to correct
defects such as non-uniform illumination, low contrast and muted colors.
In this algorithm which is based on the enhancement, each disturbance is corrected
sequentially. The first step is to remove the moiré effect is not applied, because in
our conditions this effect is not visible. Then, a homomorphic filter or frequency is
applied to remove the defects of non-uniformity of illumination and to enhance the
contrast in the image.
Regarding the acquisition noise, often present in images, they applied a wavelet
denoising followed by anisotropic filtering to eliminate unwanted oscillations. To
finalize the processing chain, a dynamic expansion is applied to increase contrast,
and equalizing the average colors in the image is being implemented to mitigate the
dominant color. Fig. 14.1 shows the result of applying the Bazeille et al. algorithm.
To optimize the computation time, all treatments are applied on the component
Y in YCbCr space. However the use of homomorphic filter changes the geometry,
which will add errors on measures after the 3D reconstruction of the scene, so we
decided not to use this algorithm.
223
The ability to measure and model large underwater sites in a short time opens up
many scientific challenges such as image processing, multimodal adjustment, land
visualization and offers new opening to marine biology, underwater archaeology and
underwater industry (offshore, harbour industry, etc.).
14.1.1 Underwater Image Processing: State of Art
The underwater image pre-processing can be addressed from two different points of
view: image restoration techniques or image enhancement methods.
Fan et al. proposed a restoration method based on blind deconvolution and the
theory of Wells (Fan et al. 2010). As a first step an arithmetic mean filter is used to
perform image denoising, and then an iterative blind deconvolution using the filtered
image is carried out. The calculation of the PSF of water is done using the following
equations:
b = cω
(14.1)
H medium (ψ, R) = exp
−cR + bR
1 − exp(−2π θ0 ψ)
2π θ0 ψ
(14.2)
Where θ 0 is referred to the median scattering angle, ψ is the spatial frequency in
cycles per radian, R is distance between sensor and object, b scattering coefficient,
c attenuation coefficient and albedo ω.
Image restoration techniques need some parameters such as attenuation coefficients, scattering coefficients and depth estimation of the object in a scene. For this
reason in our works, the preprocessing of underwater image is devoted to image
enhancement methods, which do not require a priori knowledge of the environment.
Bazeille et al. (Bazeille et al. 2006) proposed an algorithm to enhance underwater
image, this algorithm is automatic and requires no parameter adjustment to correct
defects such as non-uniform illumination, low contrast and muted colors.
In this algorithm which is based on the enhancement, each disturbance is corrected
sequentially. The first step is to remove the moiré effect is not applied, because in
our conditions this effect is not visible. Then, a homomorphic filter or frequency is
applied to remove the defects of non-uniformity of illumination and to enhance the
contrast in the image.
Regarding the acquisition noise, often present in images, they applied a wavelet
denoising followed by anisotropic filtering to eliminate unwanted oscillations. To
finalize the processing chain, a dynamic expansion is applied to increase contrast,
and equalizing the average colors in the image is being implemented to mitigate the
dominant color. Fig. 14.1 shows the result of applying the Bazeille et al. algorithm.
To optimize the computation time, all treatments are applied on the component
Y in YCbCr space. However the use of homomorphic filter changes the geometry,
which will add errors on measures after the 3D reconstruction of the scene, so we
decided not to use this algorithm.