Recently, learning-based works have been widely-investigated to enhance underwater images. However, interactions between various degradation factors (e.g., color distortion and haze effects) inevitably cause negative interference during the inference phase. Thus, these works cannot fully remove degraded factors. To address this problem, we propose a novel Joint Luminance and Chrominance Learning Network (JLCL-Net). Concretely, we reformulate the task as luminance reconstruction (for haze removal), and chrominance correction (for color correction) sub-tasks by separating the luminance and chrominance (i.e., color appearance) of the underwater images. In this way, we successfully realize the disentanglement in degraded factors to avoid introducing interference. We specify the reconstruction by integrating the atmospheric scattering model, which endows the adaptive dehazing ability over different scenarios. The correction learns to compensate for color by a simple network to reverse the color attenuation process. To this end, we obtain our JLCL-Net. To better train it, we design a new multi-stage cross-space training strategy, which progressively updates the network parameters to enlarge the network potentiality. Extensive evaluations are presented to fully verify our superiority against other methods.

中文翻译：

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联合亮度和色度学习以增强水下图像

近来，已经广泛研究了基于学习的作品以增强水下图像。但是，在推理阶段，各种降级因素（例如颜色失真和雾度效应）之间的相互作用不可避免地会引起负面干扰。因此，这些工作不能完全消除退化因素。为了解决这个问题，我们提出了一个新颖的联合亮度和色度学习网络（JLCL-Net）。具体而言，我们通过分离水下图像的亮度和色度（即颜色外观），将任务重新构造为亮度重建（用于除雾）和色度校正（用于颜色校正）子任务。这样，我们成功实现了降级因子的解缠，避免引入干扰。我们通过整合大气散射模型来指定重建，这赋予了在不同情况下的自适应除雾能力。校正学习通过简单的网络来补偿颜色，以逆转颜色衰减过程。为此，我们获得了JLCL-Net。为了更好地训练它，我们设计了一种新的多阶段跨空间训练策略，该策略逐步更新网络参数以扩大网络潜力。进行了广泛的评估，以充分证明我们相对于其他方法的优越性。