Diving Deeper into Underwater Image Enhancement: A Survey <s> UIE-DAL <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-DAL <s> Raw underwater images are degraded due to wavelength dependent light attenuation and scattering, limiting their applicability in vision systems. Another factor that makes enhancing underwater images particularly challenging is the diversity of the water types in which they are captured. For example, images captured in deep oceanic waters have a different distribution from those captured in shallow coastal waters. Such diversity makes it hard to train a single model to enhance underwater images. In this work, we propose a novel model which nicely handles the diversity of water during the enhancement, by adversarially learning the content features of the images by disentangling the unwanted nuisances corresponding to water types (viewed as different domains). We use the learned domain agnostic features to generate enhanced underwater images. We train our model on a dataset consisting images of 10 Jerlov water types. Experimental results show that the proposed model not only outperforms the previous methods in SSIM and PSNR scores for almost all Jerlov water types but also generalizes well on real-world datasets. The performance of a high-level vision task (object detection) also shows improvement using enhanced images with our model. <s> BIB002
Underwater Image Enhancement using Domain Adversarial Learning (UIE-DAL) BIB002 aims to learn agnostic model where it can enhance any underwater-type image. The backbone architecture of the UIE-DAL BIB002 is the famous encoder-decoder UNET BIB001 . The novelty of this work is the incorporation of a neural network classifier, named nuisance classifier, which classifies the latent vector extracted from the encoder. The authors claim the model to be agnostic considering that nuisance classifier is not aware of the underwater type as it receives the latent vector from the encoder, which is agnostic to the features of the underwater types. The UIE-DAL BIB002 combines three losses i.e. 2 , nuisance loss, and adversarial loss. The training is achieved in two steps. First, the only encoder-decoder structure is trained, then a nuisance classier is incorporated in the network.