Diving Deeper into Underwater Image Enhancement: A Survey <s> DUIENet <s> This paper describes a novel strategy to enhance underwater videos and images. Built on the fusion principles, our strategy derives the inputs and the weight measures only from the degraded version of the image. In order to overcome the limitations of the underwater medium we define two inputs that represent color corrected and contrast enhanced versions of the original underwater image/frame, but also four weight maps that aim to increase the visibility of the distant objects degraded due to the medium scattering and absorption. Our strategy is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. Our fusion framework also supports temporal coherence between adjacent frames by performing an effective edge preserving noise reduction strategy. The enhanced images and videos are characterized by reduced noise level, better exposed-ness of the dark regions, improved global contrast while the finest details and edges are enhanced significantly. In addition, the utility of our enhancing technique is proved for several challenging applications. <s> BIB001 </s> Diving Deeper into Underwater Image Enhancement: A Survey <s> DUIENet <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
More recently, Li et al.  constructed a real-world underwater image enhancement dataset, including 950 underwater images, 890 of which have the corresponding reference images. These potential reference images are produced by 12 image enhancement methods, and the final references are selected by 50 volunteers via majority voting. Inspired by fusion-based underwater image enhancement method BIB001 , Li et al.  proposed a gated fusion CNN trained by the constructed dataset for underwater image enhancement, called DUIENet. First, three input versions are generated by sequentially applying White Balance, Histogram Equalization, and Gamma Correction algorithms to the raw input image. Then, the DUIENet learns three confidence maps, which determine the most important features remaining in the final result. The DUIENet is a multi-scale FCNN, which consists of 14 convolutional layers followed ReLU except for the last layer (followed by Sigmoid). To reduce the color casts and artifacts introduced by the three pre-processing algorithms, three feature transformation units (FTUs) are used in the DUIENet  . The FTU includes three stacked multi-scale convolutional layers. The input of each FTU is the corresponding preprocessed underwater image, and its output is the transformed image. At last, the transformed three inputs are multiplied by the three learned confidence maps, and then the summation of the three products is the enhanced underwater image. With the constructed dataset, the authors selected 800 pairs of images randomly to generate the training set. These images are resized to 112×11 and data augmentation is used to obtain seven additional versions of the original 800 pairs of training data. The rest 90 pairs of images are treated as the testing set. To reduce the artifacts induced by pixel-wise loss functions, the authors minimize the perceptual loss (layer relu5 4 of the pre-trained VGG19 network BIB002 ).