论文标题
MLFCGAN：水下图像颜色校正的多级特征基于融合的条件gan
MLFcGAN: Multi-level Feature Fusion based Conditional GAN for Underwater Image Color Correction
论文作者
论文摘要
水下图像的颜色校正已经获得了越来越多的兴趣，因为它在促进可用的水下情景成熟视觉算法中的关键作用。受到许多视觉任务中深度卷积神经网络（DCNN）技术的惊人成功的启发，尤其是在多个尺度中提取特征方面的强度，我们提出了一个基于条件性生成对抗网络（GAN）的深层多尺度特征融合网，以进行水下图像颜色校正。在我们的网络中，首先提取多尺度功能，然后提取具有全局功能的每个尺度上的本地功能。该设计经过验证以促进更有效，更快的网络学习，从而在颜色校正和细节保存方面具有更好的性能。我们进行了广泛的实验，并与最先进的方法进行了定量和定性的比较，这表明我们的方法取得了重大改进。
Color correction for underwater images has received increasing interests, due to its critical role in facilitating available mature vision algorithms for underwater scenarios. Inspired by the stunning success of deep convolutional neural networks (DCNNs) techniques in many vision tasks, especially the strength in extracting features in multiple scales, we propose a deep multi-scale feature fusion net based on the conditional generative adversarial network (GAN) for underwater image color correction. In our network, multi-scale features are extracted first, followed by augmenting local features on each scale with global features. This design was verified to facilitate more effective and faster network learning, resulting in better performance in both color correction and detail preservation. We conducted extensive experiments and compared with the state-of-the-art approaches quantitatively and qualitatively, showing that our method achieves significant improvements.
