论文标题
DGD-CGAN：图像脱水和修复的双发电机
DGD-cGAN: A Dual Generator for Image Dewatering and Restoration
论文作者
论文摘要
水下图像通常覆盖着蓝绿色的颜色，使它们变形，模糊或对比度低。这种现象是由于水柱中的散射和吸收给出的光衰减而发生的。在本文中，我们提出了一种用于脱水的图像增强方法，该方法采用了有条件的生成对抗网络（CGAN）和两个发电机。我们的双发电机脱水CGAN（DGD-CGAN）去除了水柱引起的雾糊状和颜色，并恢复了水下场景的真实颜色，从而在水下图像中发生的各种衰减和散射现象的影响是由两个发生器处理的。第一个发电机将在输入水下图像上采用并预测脱水的场景，而第二发生物通过基于传输和图像形成模型的蔬菜光组件实现自定义损失函数来学习水下图像形成过程。我们的实验表明，与几个广泛可用的数据集中的最新方法相比，DGD-CGAN始终提供了改进的余地。
Underwater images are usually covered with a blue-greenish colour cast, making them distorted, blurry or low in contrast. This phenomenon occurs due to the light attenuation given by the scattering and absorption in the water column. In this paper, we present an image enhancement approach for dewatering which employs a conditional generative adversarial network (cGAN) with two generators. Our Dual Generator Dewatering cGAN (DGD-cGAN) removes the haze and colour cast induced by the water column and restores the true colours of underwater scenes whereby the effects of various attenuation and scattering phenomena that occur in underwater images are tackled by the two generators. The first generator takes at input the underwater image and predicts the dewatered scene, while the second generator learns the underwater image formation process by implementing a custom loss function based upon the transmission and the veiling light components of the image formation model. Our experiments show that DGD-cGAN consistently delivers a margin of improvement as compared with the state-of-the-art methods on several widely available datasets.
