Diving Deeper into Underwater Image Enhancement: A Survey <s> UIE-sGAN <s> There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net . <s> BIB001 </s> Diving Deeper into Underwater Image Enhancement: A Survey <s> UIE-sGAN <s> This letter reports on WaterGAN, a generative adversarial network (GAN) for generating realistic underwater images from in-air image and depth pairings in an unsupervised pipeline used for color correction of monocular underwater images. Cameras onboard autonomous and remotely operated vehicles can capture high-resolution images to map the seafloor; however, underwater image formation is subject to the complex process of light propagation through the water column. The raw images retrieved are characteristically different than images taken in air due to effects, such as absorption and scattering, which cause attenuation of light at different rates for different wavelengths. While this physical process is well described theoretically, the model depends on many parameters intrinsic to the water column as well as the structure of the scene. These factors make recovery of these parameters difficult without simplifying assumptions or field calibration; hence, restoration of underwater images is a nontrivial problem. Deep learning has demonstrated great success in modeling complex nonlinear systems but requires a large amount of training data, which is difficult to compile in deep sea environments. Using WaterGAN, we generate a large training dataset of corresponding depth, in-air color images, and realistic underwater images. These data serve as input to a two-stage network for color correction of monocular underwater images. Our proposed pipeline is validated with testing on real data collected from both a pure water test tank and from underwater surveys collected in the field. Source code, sample datasets, and pretrained models are made publicly available. <s> BIB002 </s> Diving Deeper into Underwater Image Enhancement: A Survey <s> UIE-sGAN <s> This paper addresses the problem of jointly haze detection and color correction from a single underwater image. We present a framework based on stacked conditional Generative adversarial networks (GAN) to learn the mapping between the underwater images and the air images in an end-to-end fashion. The proposed architecture can be divided into two components, i.e., haze detection sub-network and color correction sub-network, each with a generator and a discriminator. Specifically, a underwater image is fed into the first generator to produce a hazing detection mask. Then, the underwater image along with the predicted mask go through the second generator to correct the color of the underwater image. Experimental results show the advantages of our proposed method over several state-of-the-art methods on publicly available synthetic and real underwater datasets. <s> BIB003
Yu et al. BIB003 proposed an underwater image enhancement system using stacked conditional generative adversarial networks, abbreviated as UIE-sGAN. The proposed network architecture consists of two subnetworks i.e. haze detection subnetwork and color correction subnetwork. Each subnetwork has a generator and discriminator, and the color correction subnetwork is stacked on the haze detection subnetwork. For the haze detection subnet, the generator is similar to UNET BIB001 consisting of seven convolutional layers and seven deconvolutional layers, both followed by BN and leaky ReLU except the first convolutional layer where only leaky ReLU is employed and the last deconvolutional layer where TanH nonlinear function is realized. While the discriminator is made of four convolutional layers where the initial layer has leaky ReLU purely, and the subsequent ones have batch normalization and leaky ReLU followed by a sigmoid layer. The output of the haze detection network is a haze mask. The structure of haze detection subnet and the color-correction subnet is identical except that color-correction subnet takes the haze mask and RGB images as input and outputs a color corrected underwater image. The UIE-sGAN BIB003 has three losses i.e. the adversarial loss for each network and a consistency loss. The * , Chongyi Li * training is accomplished by using WaterGAN BIB002 to generate underwater images from NYU-v2 dataset  . Out of 1449 images, 1200 are held for training while the network is evaluated on the remaining ones. The images are resized to 286×286 and then cropped to 256×256 and further applying data augmentation. The network is optimized using ADAM by fixing the learning rate as 5×10 −5 .