针对水下光学图像颜色失真、非均匀光照、对比度低的问题，提出基于优势特征图像融合的水下光学图像增强算法.首先，提出改进的暗通道先验算法去除退化图像中的不均匀浑浊并均衡色彩；其次，对颜色校正图像分别使用基于加权分布的自适应伽玛校正算法和限制对比度自适应直方图均衡-同态滤波算法，增强颜色校正图像对比度并使其亮度均衡；最后，定义三幅融合图像即颜色校正图像、亮度均衡图像、对比度增强图像的关联权重图，通过多尺度融合算法获得融合图像.与单一预处理算法只能解决对应的退化现象相比，该算法对单幅退化图像进行多算法处理，得到三幅优势特征图像，通过不同权重的组合最大程度地将各优势特征相结合，得到的综合效果远超各单一算法优化效果，不再局限于解决颜色失真等单一问题.将本文算法与现有算法在主观评价和客观评价两方面进行实验对比，结果表明，该算法可以有效平衡水下图像的色度、饱和度及清晰度，视觉效果接近自然场景下的图像.

Aiming at the problems of color distortion, uneven illumination and low contrast of underwater optical image, an underwater optical image enhancement algorithm based on the fusion of dominant feature image was proposed. Firstly, an improved dark channel prior algorithm was proposed to remove the uneven turbidity and balance the color in the degraded image. Secondly, the adaptive gamma correction algorithm based on weighted distribution and the contrast limited adaptive histogram equalization-homomorphic filtering algorithm were used to enhance the contrast of color correction image and make its brightness distribution uniform. Finally, the associated weight maps of the three fused images namely the color-corrected image, the brightness-balanced image and the contrast-enhanced image were defined, and the fused images were obtained by the multi-scale fusion algorithm. Compared with single preprocessing algorithm which can only solve the corresponding degradation phenomenon, the algorithm processes single degraded image with multiple algorithms and obtains three dominant feature images, the combination of different weights can combine the dominant features to the greatest extent, and the comprehensive effect is far beyond the optimization effect of each single algorithm, and is no longer limited to solving single problems such as color distortion. The algorithm in this paper is compared with existing algorithms in subjective evaluation and objective evaluation. The results show that the algorithm can effectively balance the chroma, saturation and sharpness of underwater images, and the visual effect is close to the images in natural scenes.