Zhou Jingchun, Yang Tongyu, Ren Wenqi, Zhang Dan, Zhang Weishi
Opt Express. 2021 Sep 13;29(19):29864-29886. doi: 10.1364/OE.427839.
For the enhancement process of underwater images taken in various water types, previous methods employ the simple image formation model, thus obtaining poor restoration results. Recently, a revised underwater image formation model (i.e., the Akkaynak-Treibitz model) has shown better robustness in underwater image restoration, but has drawn little attention due to its complexity. Herein, we develop a dehazing method utilizing the revised model, which depends on the scene depth map and a color correction method to eliminate color distortion. Specifically, we first design an underwater image depth estimation method to create the depth map. Subsequently, according to the depth value of each pixel, the backscatter is estimated and removed by the channel based on the revised model. Furthermore, we propose a color correction approach to adjust the global color distribution of the image automatically. Our method only uses a single underwater image as input to eliminate lightwave absorption and scattering influence. Compared with state-of-the-art methods, both subjective and objective experimental results show that our approach can be applied to various real-world underwater scenes and has better contrast and color.
对于在各种水体类型中拍摄的水下图像增强过程，以往的方法采用简单的图像形成模型，因此恢复效果较差。最近，一种改进的水下图像形成模型（即Akkaynak-Treibitz模型）在水下图像恢复中表现出更好的鲁棒性，但由于其复杂性而很少受到关注。在此，我们开发了一种利用该改进模型的去雾方法，该方法依赖于场景深度图和一种颜色校正方法来消除颜色失真。具体而言，我们首先设计一种水下图像深度估计方法来创建深度图。随后，根据每个像素的深度值，基于改进模型通过通道估计并去除后向散射。此外，我们提出一种颜色校正方法来自动调整图像的全局颜色分布。我们的方法仅使用单幅水下图像作为输入来消除光波吸收和散射的影响。与现有方法相比，主观和客观实验结果均表明，我们的方法可应用于各种实际水下场景，并且具有更好的对比度和色彩。
