MLFcGAN: Multilevel Feature Fusion-Based Conditional GAN for Underwater Image Color Correction

Color correction for underwater images has received increasing interest, due to its critical role in facilitating available mature vision algorithms for underwater scenarios. Inspired by the stunning success of deep convolutional neural network (DCNN) techniques in many vision tasks, especially the strength in extracting features in multiple scales, we propose a deep multiscale feature fusion net based on the conditional generative adversarial network (GAN) for underwater image color correction. In our network, multiscale features are extracted first, followed by augmenting local features in 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 the results with state-of-the-art approaches quantitatively and qualitatively, showing that our method achieves significant improvements.