  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.
