Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazy image formation model is widely used to restore the image quality. It depends on two optical parameters: the background light (BL) and the transmission map (TM). Underwater images can also be enhanced by color and contrast correction from the perspective of image processing. In this paper, we propose an effective underwater image enhancement method for underwater images in composition of underwater image restoration and color correction. Firstly, a manually annotated background lights (MABLs) database is developed. With reference to the relationship between MABLs and the histogram distributions of various underwater images, robust statistical models of BLs estimation are provided. Next, the TM of R channel is roughly estimated based on the new underwater dark channel prior (NUDCP) via the statistic of clear and high resolution (HD) underwater images, then a scene depth map based on the underwater light attenuation prior (ULAP) and an adjusted reversed saturation map (ARSM) are applied to compensate and modify the coarse TM of R channel. Next, TMs of G-B channels are estimated based on the difference of attenuation ratios between R and G-B channels. Finally, to improve the color and contrast of the restored image with a dehazed and natural appearance, a variation of white balance is introduced as post-processing. In order to guide the priority of underwater image enhancement, sufficient evaluations are conducted to discuss the impacts of the key parameters including BL and TM, and the importance of the color correction. Comparisons with other state-of-the-art methods demonstrate that our proposed underwater image enhancement method can achieve higher accuracy of estimated BLs, lower computation time, overall superior performance, and better information retention.

中文翻译：

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基于背景光统计模型的水下图像增强及透射图优化

由于水介质中的光吸收和散射，水下图像通常会出现严重的质量下降和失真。模糊图像形成模型被广泛用于恢复图像质量。它取决于两个光学参数：背景光 (BL) 和透射图 (TM)。从图像处理的角度，还可以通过颜色和对比度校正来增强水下图像。在本文中，我们提出了一种有效的水下图像增强方法，包括水下图像恢复和色彩校正。首先，开发了手动注释的背景灯（MABL）数据库。参考MABLs与各种水下图像直方图分布之间的关系，提供了BLs估计的鲁棒统计模型。下一个，通过清晰高分辨率（HD）水下图像的统计，基于新的水下暗通道先验（NUDCP）粗略估计R通道的TM，然后基于水下光衰减先验（ULAP）和一个场景深度图应用调整后的反向饱和度图 (ARSM) 来补偿和修改 R 通道的粗略 TM。接下来，基于R和GB通道之间衰减比的差异估计GB通道的TM。最后，为了改善具有去雾和自然外观的恢复图像的颜色和对比度，引入了白平衡的变化作为后处理。为了指导水下图像增强的优先级，进行了充分的评估，讨论了包括BL和TM在内的关键参数的影响，以及色彩校正的重要性。