Diving Deeper into Underwater Image Enhancement: A Survey <s> UWCNN <s> We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm. <s> BIB001 </s> Diving Deeper into Underwater Image Enhancement: A Survey <s> UWCNN <s> In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts. To address this problem, we propose a convolutional neural network based image enhancement model, i.e., UWCNN, which is trained efficiently using a synthetic underwater image database. Unlike the existing works that require the parameters of underwater imaging model estimation or impose inflexible frameworks applicable only for specific scenes, our model directly reconstructs the clear latent underwater image by leveraging on an automatic end-to-end and data-driven training mechanism. Compliant with underwater imaging models and optical properties of underwater scenes, we first synthesize ten different marine image databases. Then, we separately train multiple UWCNN models for each underwater image formation type. Experimental results on real-world and synthetic underwater images demonstrate that the presented method generalizes well on different underwater scenes and outperforms the existing methods both qualitatively and quantitatively. Besides, we conduct an ablation study to demonstrate the effect of each component in our network. <s> BIB002
To deal with the low contrast and distorted color of the degraded underwater images, Anwar et al. BIB002 proposed a CNN underwater image enhancement model, called UWCNN. The UWCNN is an end-to-end model trained by the synthetic underwater image datasets, which includes three densely connected building blocks. Furthermore, each basic building block consists of three densely connected convolutional layers. After the three chained building blocks, a convolutional layer is used to learn the difference (residual) between the degraded underwater image and its clean counterpart. To train the UWCNN BIB002 model, the authors use the attenuation coefficients of different water types to synthesize various underwater image datasets according to the underwater image formation model resulting in ten types of underwater image datasets which are synthesized by using the RGB-D NYU-v2 dataset  . These underwater image datasets simulate the open ocean water types and coastal water types ranging from the clearest to the most turbid. Finally, the authors train ten UWCNN models for the ten types of underwater images. The parameters of the UWCNN model are learned by joint optimizing the 2 and SSIM loss functions. In the entire UWCNN, the kernel sizes and filter numbers are fixed, i.e., 3×3 and 16, respectively. The learning rate is set to 2×10 −4 and ADAM BIB001 is used for optimization in TensorFlow framework.