Objective

2

Underwater image is an important carrier of ocean information

and clear images obtained underwater play a critical role in ocean engineering

such as underwater device inspection and marine biological recognition. However

compared with images captured in terrestrial environment

underwater images often exhibit color shift

low contrast

and poor visibility because the light is absorbed

scattered

and reflected by the water medium when traveling from an object to a camera in the complicated underwater environment. Existing methods cannot be effective and suitable for different types of underwater images. To address these problems

we propose a simple underwater image enhancement method using adaptive histogram stretching in different color models

which can improve the contrast and brightness of the underwater image

reduce the introduction of noise

and generate a relatively natural image.

Method

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Given that images are rarely color balanced in the underwater situation

we first preprocess the underwater image with color equalization in the red

green

and blue (RGB) color model based on gray world assumption theory. Color equalization is employed only on the green and blue channels of the input image to avoid the inappropriate compensation for the red channel in the water

which is often achieved by simple color balancing. Then

we analyze the distribution characteristics of the RGB channels

which are focused on the regular range. Meanwhile

we determine the rule of selective attenuation in three channels of the underwater image

in which the red color is seriously affected and the wavelength of the red color is the longest

leading to most underwater images having a blue-green tone. On the basis of the results and analysis

we propose an adaptive histogram stretching approach in the RGB color model to adapt to different underwater images. Given that underwater images are disturbed by various factors

the stretching range is limited to the range[0.5%

99.5%] and is obtained based on the inherent characteristics similar to the variation of Rayleigh distribution to reduce the effect of several extreme pixels on the process of adaptive histogram stretching. The desired range of each channel is acquired according to Rayleigh distribution theory

the image formation model

and the residual energy ratios of different color channels underwater. These dynamic stretching ranges have considered the characteristics of histogram distribution in hazed image and in the expected output image simultaneously. Finally

four possible situations of histogram stretching on the basis of the desired range are introduced to preserve the enhanced underwater images from over-stretching or under-stretching. Although the smart method based on adaptive histogram stretching will not introduce obvious noise to the output image

the guided filter is employed to eliminate the effect of noise to improve the contrast and capture relevant details of the image. Then

in the CIE-Lab color model

the "L" luminance component

which is equivalent to image luminance

is applied with linear normalization in the stretch range[0.1%

99.9%]

and the brightness of the entire image is significantly improved. The "a" and "b" color components are modified to acquire the appropriate color correction using the exponential model curve function. Ultimately

a color-equalized

contrast-enhanced

and brightness-corrected underwater image can be produced as the perceivable output image.

Result

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Our proposed method is evaluated by comparing it with two effective nonphysical methods and two state-of-the-art physical methods qualitatively and quantitatively. The integrated color model and the unsupervised color correction model

as typical nonphysical methods

are most similar to the proposed method in terms of histogram modification. The blind global histogram stretching usually tends to produce output images that contain under-enhanced or over-enhanced and undersaturated or oversaturated areas and high noise. The dark channel prior-based and underwater dark channel prior-based underwater image restoration are imposed to estimate the background light and transmission map (TM) to restore underwater images based on the optical physical model. Physical methods are appropriate only for the enhancement and restoration of certain underwater images under specific circumstances and are time consuming for estimating the TM. Experimental results of different types of underwater images

such as brown coral

underwater fishes

and stones with different color tones

show that our proposed method can achieve better enhanced quality. Our method obtains the highest average subjective quality score among the underwater image enhancement and restoration methods

further proving that our method exhibits the best visual effects. The proposed method is not only simple and effective but also improves the contrast

details

and colors of the input images. In quantitative assessment

the maximum value of UCIQE represents the balance of the chroma

saturation

and contrast of the enhanced image in all methods; the highest value of ENTROPY means that our method preserves the richest information and details; the lowest value of Q-MOS indicates better perceptual quality; and the lowest value of MSE and the highest value of the peak signal-to-noise ratio can reduce the introduction of noise when the original image is enhanced based on adaptive histogram stretching. In summary

the final results have shown that our method can recover natural underwater images

enhance the visibility of hazed images

and produce high-quality underwater images.

Conclusion

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The proposed method consists of two parts

i.e.

color correction and contrast enhancement in the RGB color model and modification of the brightness and hue in the CIE-Lab color model. In the RGB color model

adaptive histogram stretching is proposed with reasonable consideration of the distribution characteristics of the underwater image and the physical model of underwater image degradation. In the CIE-Lab color model

the stretching and S-model curve functions are adopted to modify the luminance and colors. The proposed method can be of low complexity

can be appropriate for different underwater images under complicated scenarios

and can effectively enhance visibility according to the best perceptual quality

high contrast

and most information and details. Our method achieves impressive results for applicability and robustness compared with the representative underwater image enhancement and restoration methods. Despite its satisfactory performance

our method still needs some improvements:1) the influences of the distance from the object to the water surface and artificial light on the results of restoration and enhancement are all ignored to some extent; 2) the noise due to histogram stretching cannot be entirely removed based on the guided filter; 3) in deep ocean

the radiation of natural light spreading from the water surface to the object fades away and the artificial light becomes the main light source for underwater imaging. These limitations will be investigated

and the proposed method will be refined in future work.