Diving Deeper into Underwater Image Enhancement: A Survey <s> URCNN <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> BIB001 </s> Diving Deeper into Underwater Image Enhancement: A Survey <s> URCNN <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> BIB002 </s> Diving Deeper into Underwater Image Enhancement: A Survey <s> URCNN <s> Improving the quality of underwater image has a significant impact on many signal processing and computer vision applications, while haze-effect and color shift are main handicaps need to be surmounted. Due to the complexity of the underwater environmental factors, most existing image enhancement techniques cannot be directly applied to address this task. In this work, we develop a novel framework to jointly performing residual learning on transmission and image domains for underwater scene entrenchment. Indeed, our deep model consists of a data-driven residual architecture for transmission estimation and a knowledge-driven scene residual formulation for underwater illumination balance. Therefore, we can aggregate the prior knowledge and data information to investigate the underlying underwater image distribution. Moreover, by introducing adaptive exposure map, image colors will also be corrected accordingly. Experimentally, both quantitative and qualitative analysis can indicate outstanding effectiveness of the proposed algorithm, against state-of-the-art approaches. <s> BIB003
Underwater residual convolutional neural network (UR-CNN) BIB003 is proposed by Hou et al., which aims to learn the transmission map. The URCNN, in the first, uses a convolutional layer followed by ReLU to extract * , Chongyi Li * features. The batch normalization and ReLU succeed the second Conv layer. This pattern is repeated until the reconstruction layer, where only the convolutional layer is employed to output the transmission map. A global skip connection is used to enforce residual learning. The output transmission map is used to refine the input image. The network architecture of the URCNN is a modified version of VGG BIB001 and the input to the network is 180×180 transmission map instead of the original image. The underwater images are generated from randomly selected 1000 NYU dataset  images. Furthermore, using random medium attenuation coefficient and background light, a total of 1800 images are generated for training and 200 images for testing. The initial learning rate is selected to be 10 −1 and reduced to 10 for 60 epochs. The depth of the network is 25 layers with each layer having 64 feature maps and a filter size of 3×3. Similar to BIB002 , the loss used for learning is 2 .