Diving Deeper into Underwater Image Enhancement: A Survey <s> Introduction <s> Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance, in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers can not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications. <s> BIB001 </s> Diving Deeper into Underwater Image Enhancement: A Survey <s> Introduction <s> Rapid development of affordable and portable consumer depth cameras facilitates the use of depth information in many computer vision tasks such as intelligent vehicles and 3D reconstruction. However, depth map captured by low-cost depth sensors (e.g., Kinect) usually suffers from low spatial resolution, which limits its potential applications. In this paper, we propose a novel deep network for depth map super-resolution (SR), called DepthSR-Net. The proposed DepthSR-Net automatically infers a high-resolution (HR) depth map from its low-resolution (LR) version by hierarchical features driven residual learning. Specifically, DepthSR-Net is built on residual U-Net deep network architecture. Given LR depth map, we first obtain the desired HR by bicubic interpolation upsampling and then construct an input pyramid to achieve multiple level receptive fields. Next, we extract hierarchical features from the input pyramid, intensity image, and encoder–decoder structure of U-Net. Finally, we learn the residual between the interpolated depth map and the corresponding HR one using the rich hierarchical features. The final HR depth map is achieved by adding the learned residual to the interpolated depth map. We conduct an ablation study to demonstrate the effectiveness of each component in the proposed network. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods. In addition, the potential usage of the proposed network in other low-level vision problems is discussed. <s> BIB002
'Sit, be still, and listen.' Rumi Nowadays, developing, exploring, and protecting the ocean's resources have become the strategy center in the international community. Clear underwater images and videos can provide valuable information of the underwater world, which are essential for numerous engineering and research tasks such as underwater archaeology, underwater surveillance, etc. However, the raw underwater images and videos usually suffer from the effects of quality degradation, especially the impact of backscatter in far distances. The issues of quality degradation are mainly introduced by light selective absorption and scattering in water as well as the use of artificial light in deep water. The degraded underwater images have low contrast and brightness, color deviations, blurry details, and uneven bright speck, which limit their applications in practical scenarios. As an indispensable processing step, underwater image enhancement methods ranging from the conventional techniques (e.g., physical model-based methods, and histogram equalizationbased methods) to the data-driven techniques (e.g., convolutional neural networks, and generative adversarial networks) have been attracting increasing attention. The past few decades have seen the rapid development of deep learning techniques, which have been extensively applied in various computer vision and image processing tasks [31] . Deep learning has significantly improved the performance of high-level vision tasks such as object detection [47] and object recognition  . Moreover, the low-level vision tasks, such as image superresolution BIB002 and image denoising BIB001 , also benefit from the advantages of deep networks and deliver stateof-the-art performance. Unfortunately, we are unable to observe the appealing performance of deep learningbased underwater image enhancement, although lots of researchers have attempted to utilize the deep learning techniques to the underwater image enhancement. In this paper, we mainly focus on deep learning method, which enhance and restore underwater images. Through this exposition, we provide the latest development and comparison of current deep underwater image restoration and enhancement algorithms. Furthermore, we summarize the existing issues, analyze the potential reasons, and suggest future research directions. The main contributions of this paper are two-fold: • We summarize the deep learning-based underwater image enhancement algorithms, including network architectures, network parameters, training data, loss function, and training configurations. It provides, to the best of our knowledge, the first comprehensive and in-depth survey for deep learning-based underwater image enhancement, which is helpful for developing more robust and effective deep algorithms. • We conduct the systematic experiments on diverse datasets to qualitatively and quantitatively compare the deep learning-based underwater image enhancement algorithms. Our evaluation and analysis demonstrate the performance of current deep algorithms, point out their limitations, and indicates the bias of existing benchmark datasets and evaluation metrics. As a consequence, we give potential insights for future research directions in this field of study. The rest of the paper is organized as follows. Section 2 introduces the background of underwater image enhancement and restoration, mainly focusing on the imaging models. Section 3 presents the existing deep learning-based underwater image enhancement algorithms and insights into the network. Section 4 gives the experimental quantitative and qualitative results and analysis, evaluation metrics, and datasets. Section 5 suggests future research directions, and Section 6 concludes this paper.