Diving Deeper into Underwater Image Enhancement: A Survey <s> UIR-Net <s> Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relations from a single image is less straightforward, requiring integration of both global and local information from various cues. Moreover, the task is inherently ambiguous, with a large source of uncertainty coming from the overall scale. In this paper, we present a new method that addresses this task by employing two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines this prediction locally. We also apply a scale-invariant error to help measure depth relations rather than scale. By leveraging the raw datasets as large sources of training data, our method achieves state-of-the-art results on both NYU Depth and KITTI, and matches detailed depth boundaries without the need for superpixelation. <s> BIB001 </s> Diving Deeper into Underwater Image Enhancement: A Survey <s> UIR-Net <s> In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. <s> BIB002 </s> Diving Deeper into Underwater Image Enhancement: A Survey <s> UIR-Net <s> Underwater images often suffer from color distortion and visibility degradation due to the light absorption and scattering. Existing methods utilize various assumptions/constrains to achieve reasonable solutions for underwater image enhancement. However, these methods share the common limitation that the adopted assumptions may not work for some particular scenes. To address this problem, this paper proposes an end to end framework for underwater image enhancement, where a CNN-based network called UIE-Net is presented. The UIE-net is trained with two tasks, color correction and haze removal. This unified training approach enables learning a strong feature representation for both tasks simultaneously. For better extracting the inherent features in local patches, a pixels disrupting strategy is exploited in the proposed learning framework, which significantly improves the convergent speed and accuracy. To handle the training of UIE-net, we synthesize 200000 training images based on the physical underwater imaging model. Experiments on benchmark underwater images for cross-scenes show that UIE-net achieves superior performance over existing methods. <s> BIB003 </s> Diving Deeper into Underwater Image Enhancement: A Survey <s> UIR-Net <s> Images taken underwater often suffer color distortion and low contrast because of light scattering and absorption. An underwater image can be modeled as a blend of a clear image and a background light, with the relative amounts of each determined by the depth from the camera. In this paper, we propose two neural network structures to estimate background light and scene depth, to restore underwater images. Experimental results on synthetic and real underwater images demonstrate the effectiveness of the proposed method. <s> BIB004
Cao et al. BIB004 lately developed a deep network for underwater image restoration inspired by classical methods where the transmission map and the background light are estimated and computed independently. Consequently, two different network architectures were proposed i.e. the light network (BL-Net) and the transmission map network (TM-Net) while collectively, the network is called UIR-Net BIB004 . The background light network (BL-Net) is simple and consists of five layers. The initial three layers are convolutional with BN and pooling. The last two layers are fully connected ones. The output of this BL-Net is thresholded to constrain it, in the range of [0,1]. The transmission map network (TM-Net) is more complicated and is based on BIB001 , consisting of two subnets, i.e., coarse-global subnet, and refine subnet. The coarse subnet is made of five convolutional layers, with the first two convolutional layers having pooling and batch normalization. The last layers of the coarse-global subnet are fully connected ones. The refined subnet has three convolutional layers and an upsampling layer which lies before the final convolutional layer. The output of this network is the depth map. Using depth maps, the transmission maps are computed. As a last preprocessing step, the guided filter  is applied to refine the maps further. The loss for the BL-Net is Euclidean while for the TM-Net is a scale-invariant minimum square error (MSE) adopted from Eigen et al. BIB001 . Similar to BIB003 , UIRNet BIB004 use NYU-v2 dataset  to generate 12,000 synthetic underwater images using a total of 29 different underwater ambient lights. The BL-Net is initialized randomly, while TM-Net utilizes the weights from VGG BIB002 .