黄冬梅,王龑,宋巍,王振华,杜艳玲(上海海洋大学信息学院, 上海 201306)
目的 水下图像是海洋信息的重要载体，然而与自然环境下的图像相比，其成像原理更复杂、对比度低、可视性差。为保证不同类型水下图像的增强效果，本文提出在两种颜色模型下自适应直方图拉伸的水下图像增强方法。方法 首先，进行基于Gray-World理论对蓝、绿色通道进行颜色均衡化预处理。然后，根据红绿蓝（R-G-B）通道的分布特性和不同颜色光线在水下传播时的选择性衰减，提出基于参数动态优化的R-G-B颜色模型自适应直方图拉伸，并采用引导滤波器降噪。接下来，在CIE-Lab颜色模型，对‘L’亮度和‘a’‘b’色彩分量分别进行线性和曲线自适应直方图拉伸优化。最终，增强的水下图像呈现出高对比度、均衡的饱和度和亮度。结果 选取不同类型的水下图像作为数据集，将本文方法与融合颜色模型（ICM）、非监督颜色纠正模型（UCM）、基于暗通道先验性（DCP）的水下图像复原和基于水下暗通道先验（UDCP）的图像复原方法相比较，增强后的图像具有高对比度和饱和度。定性和定量分析实验结果说明本文提出的方法能够获得更好视觉效果，增强后的图像拥有更高信息熵和较低噪声。结论 在RGB颜色模型中，通过合理地考虑水下图像的分布特性和水下图像退化物理模型提出自适应直方图拉伸方法；在CIE-Lab颜色模型中，引入拉伸函数和指数型曲线函数重分布色彩和亮度两个分量，本方法计算复杂度低，适用于不同复杂环境下的水下图像增强。
Underwater image enhancement method using adaptive histogram stretching in different color models
Huang Dongmei,Wang Yan,Song Wei,Wang Zhenhua,Du Yanling(College of Information Technology, Shanghai Ocean University, Shanghai 201306, China)
Objective 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 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 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 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.