The captured underwater images suffer from color cast and haze effect caused by absorption and scattering. These interdependent phenomena jointly degrade images, resulting in failure of autonomous machines to recognize image contents. Most existing learning-based methods for underwater image enhancement (UIE) treat the degraded process as a whole and ignore the interaction between color correction and dehazing. Thus, they often obtain unnatural results. To this end, we propose a novel joint network to optimize the results of color correction and dehazing in multiple iterations. Firstly, a novel triplet-based color correction module is proposed to obtain color-balanced images with identical distribution of color channels. By means of inherent constraints of the triplet structure, the information of channel with less distortion is utilized to recover the information of other channels. Secondly, a recurrent dehazing module is designed to alleviate haze effect in images, where the Gated Recurrent Unit (GRU) as the memory module optimizes the results in multiple cycles to deal with severe underwater distortions. Finally, an iterative mechanism is proposed to jointly optimize the color correction and dehazing. By learning transform coefficients from dehazing features, color features and basic features of raw images are progressively refined, which maintains color balanced during the dehazing process and further improves clarity of images. Experimental results show that our network is superior to the existing state-of-the-art approaches for UIE and provides improved performance for underwater object detection.

中文翻译：

---

联合迭代色彩校正和去雾，以增强水下图像

所捕获的水下图像会遭受由吸收和散射引起的偏色和混浊效应。这些相互依存的现象共同降低了图像质量，导致自主机器无法识别图像内容。大多数现有的基于学习的水下图像增强（UIE）方法都将降级的过程作为一个整体来处理，而忽略了色彩校正和除雾之间的相互作用。因此，他们经常获得不自然的结果。为此，我们提出了一种新颖的联合网络，以优化多次迭代中颜色校正和除雾的结果。首先，提出了一种新颖的基于三重态的色彩校正模块，以获得具有相同色彩通道分布的色彩平衡图像。通过三重态结构的固有约束，利用失真较小的信道信息来恢复其他信道的信息。其次，设计了一个循环除雾模块来减轻图像中的雾度效果，其中门控循环单元（GRU）作为存储模块可以在多个周期内优化结果，以应对严重的水下失真。最后，提出了一种迭代机制来共同优化色彩校正和除雾。通过从除雾特征中学习变换系数，可以逐渐细化原始图像的色彩特征和基本特征，从而在除雾过程中保持色彩平衡，并进一步提高图像的清晰度。实验结果表明，我们的网络优于现有的UIE最新技术，并为水下物体检测提供了改进的性能。