目的 为解决水下图像的色偏和低对比度等问题，提出一种基于双尺度图像分解的水下彩色图像增强算法。方法 通过基于均值和方差的对比度拉伸方法改善图像的色偏问题，并利用中值滤波降低红通道对比度拉伸后引入的噪声；采用双尺度图像分解绿通道图像补偿红通道图像细节；在处理后的红通道图像中引入原始图像红通道的真实细节与颜色。结果 选取不同水下图像作为实验数据集，将本文方法与暗通道先验的方法、基于融合的方法、自动红通道恢复方法以及一种基于卷积神经网络深度学习的方法相比较，首先从主观视觉效果进行定性分析，然后通过不同评测指标进行定量分析。主观定性分析结果表明，提出的方法相比较其他方法能够更好地解决图像色偏和红色阴影问题；定量分析中，自然图像质量评价（natural image quality evaluation，NIQE）指标和信息熵（information entropy，IE）值较基于融合的方法和深度学习的方法分别提高了1.8%和13.6%，且水下图像质量评价指标（underwater image quality measurement method，UIQM）较其他方法更优。结论 提出的双尺度图像分解方法利用水下图像成像特点解决图像色偏以及低对比度问题，具有良好的适应能力，同时算法复杂度低且鲁棒性较高，普遍适用于复杂的水下彩色图像增强。

Objective Due to the complex and changeable underwater environment, when light enters a water body, it is affected by absorption and scattering, which leads to color cast, blur, and low contrast of underwater images. Water has different absorption capabilities for different light rays. As a result, the red channel information of the image is poorly preserved, making underwater imaging difficult. In order to solve the problems caused by light absorption in underwater images, existing scientific research has found that compared with the red and blue channels, the obtained green channel details of the underwater channel are best preserved, whereas the red channel details are poorly preserved. According to the characteristics of underwater imaging, the present study proposes an underwater color image enhancement method based on two-scale image decomposition. The method can effectively solve the problems of color cast and low contrast of the image and reduce the introduction of red shadow to obtain high-resolution underwater images. Method First, a contrast stretching method based on the gray world hypothesis is used. The method calculates the mean and mean square error of each channel independently and then uses a new normalization method using the mean and mean square error of each channel. In order to reduce the color cast problem of the image, a median filter is used to reduce the noise problem introduced by the red channel contrast stretch. Then, the two-scale image decomposition method is used to apply a large-scale mean filter to the red channel of the median filtered image and the green channel of the image after the contrast is stretched. Subsequently, the channel is divided into a large-scale base layer and a small-scale detail layer. While retaining the large-scale base layer of the red channel of the image, the small-scale detail layer of the green channel is introduced. Because the color and details of the red channel of the original image are equally important, the true details and colors of the red channel of the original image are introduced into the red channel of the processed image to achieve a better restoration of the red channel colors and details of the image. Different underwater images are selected as the experimental dataset, and the proposed method is compared with the dark channel prior method, fusion-based method, automatic red channel recovery method, and deep learning method based on convolutional neural network. First, subjective analysis of the visual effects is conducted, and then objective analysis is performed through different underwater evaluation indicators. Result The results of different experimental images are compared. The dark channel prior method cannot solve the problem of color cast and low contrast of underwater images. The red channel recovery method solves the problem of image color cast or low contrast, but the image results obtained are poorer than those of the proposed method. The fusion-based method is better than the proposed method in improving image contrast. This is maintained by the edge of the image fusion algorithm, but an obvious red shadow problem will be observed in the resulting image. The proposed method can better solve the red shadow problem in the image. Deep learning method can improve the brightness and clarity of the image, but the result still has a certain color cast problem. Compared with the four methods, the proposed method can better solve the problem of color cast of underwater images, and no red shadow is generated, which is more in line with the visual perception of human eyes. Three methods, namely, natural image quality evaluation method (NIQE), information entropy (IE), and underwater image quality measurement method (UIQM) inspired by the human visual system, are used in the experiment for comparative analysis. NIQE compares the test image with the default model calculated from the natural scene image. The lower the index, the better the image perception quality and the higher the clarity. At the same time, NIQE and human eye subjective quality evaluation have better consistency, are similar to the human visual system, and can effectively perform real-time image quality evaluation. IE is an index to measure the information richness of a pair of images. The larger the IE value, the richer the detailed information contained in the image. Finally, UIQM is a better existing underwater image quality evaluation method, which can solve the evaluation of three important indicators of underwater images (i.e., color, clarity, and contrast). The larger the value of UIQM, the higher the clarity and the better the quality of the image. Compared with fusion-based methods and deep learning methods, NIQE and IE in quantitative analysis are improved by 1.8% and 13.6%, respectively. The value of UIQM is 1.542, which is the best result compared with other methods. The experimental results show that the proposed method can better solve the problem of image color cast and red shadow compared with other methods. Conclusion First, through the contrast-based stretching method, the green channel image with good detail retention is used to compensate for the red channel image detail. We propose an underwater color image enhancement method based on two-scale image decomposition in this study. The proposed method can better reflect the detail of the red channel and does not lose the color information of the red channel itself. Subjective qualitative analysis and quantitative analysis are used to evaluate the algorithm in this study. According to the experimental results, the proposed method has poor performance on underwater color images with poor red and blue channel information because the method focuses on restoring the details and colors of the red channel. The method is suitable for underwater color images with poor red channel information performance and relatively good green channel and blue channel information performance. Therefore, considering only the red channel may be too restrictive. Next work, we consider the influence of the blue and green channels of the image on the red channel, and improve image clarity through image fusion.