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As one of the key requirements for underwater exploration, underwater depth map estimation is of great importance in underwater vision research. Although significant progress has been achieved in the fields of image-to-image translation and depth map estimation, a gap between normal depth map estimation and underwater depth map estimation still remains. Additionally, it is a great challenge to build a mapping function that converts a single underwater image into an underwater depth map due to the lack of paired data. Moreover, the ever-changing underwater environment further intensifies the difficulty of finding an optimal mapping solution. To eliminate these bottlenecks, we developed a novel image-to-image framework for underwater image synthesis and depth map estimation in underwater conditions. For the problem of the lack of paired data, by translating hazy in-air images (with a depth map) into underwater images, we initially obtained a paired dataset of underwater images and corresponding depth maps. To enrich our synthesized underwater dataset, we further translated hazy in-air images into a series of continuously changing underwater images with a specified style. For the depth map estimation, we included a coarse-to-fine network to provide a precise depth map estimation result. We evaluated the efficiency of our framework for a real underwater RGB-D dataset. The experimental results show that our method can provide a diversity of underwater images and the best depth map estimation precision.

Underwater depth prediction plays an important role in underwater vision research. Because of the complex underwater environment, it is extremely difficult and expensive to obtain underwater datasets with reliable depth annotation. Thus, underwater depth map estimation with a data-driven manner is still a challenging task. To tackle this problem, we propose an end-to-end system including two different modules for underwater image synthesis and underwater depth map estimation, respectively. The former module aims to translate the hazy in-air RGB-D images to multi-style realistic synthetic underwater images while retaining the objects and the structural information of the input images. Then we construct a semi-real RGB-D underwater dataset using the synthesized underwater images and the original corresponding depth maps. We conduct supervised learning to perform depth estimation through the pseudo paired underwater RGB-D images. Comprehensive experiments have demonstrated that the proposed method can generate multiple realistic underwater images with high fidelity, which can be applied to enhance the performance of monocular underwater image depth estimation. Furthermore, the trained depth estimation model can be applied to real underwater image depth map estimation. We will release our codes and experimental setting in https://github.com/ZHAOQIII/UW_depth .

Acquiring underwater depth maps is essential as they provide indispensable three-dimensional spatial information for visualizing the underwater environment. These depth maps serve various purposes, including underwater navigation, environmental monitoring, and resource exploration. While most of the current depth estimation methods can work well in ideal underwater environments with homogeneous illumination, few consider the risk caused by irregular illumination, which is common in practical underwater environments. On the one hand, underwater environments with low-light conditions can reduce image contrast. The reduction brings challenges to depth estimation models in accurately differentiating among objects. On the other hand, overexposure caused by reflection or artificial illumination can degrade the textures of underwater objects, which is crucial to geometric constraints between frames. To address the above issues, we propose an underwater self-supervised monocular depth estimation network integrating image enhancement and auxiliary depth information. We use the Monte Carlo image enhancement module (MC-IEM) to tackle the inherent uncertainty in low-light underwater images through probabilistic estimation. When pixel values are enhanced, object recognition becomes more accessible, allowing for a more precise acquisition of distance information and thus resulting in more accurate depth estimation. Next, we extract additional geometric features through transfer learning, infusing prior knowledge from a supervised large-scale model into a self-supervised depth estimation network to refine loss functions and a depth network to address the overexposure issue. We conduct experiments with two public datasets, which exhibited superior performance compared to existing approaches in underwater depth estimation.

The Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) is equipped with an Advanced Terrain Laser Altimeter System (ATLAS) with the capability of penetrating water bodies, making it a widely utilized tool for the bathymetry of various aquatic environments. However, the laser sensor often encounters a significant number of noise photons due to various factors such as sunlight, water quality, and after-pulse effect. These noise photons significantly compromise the accuracy of bathymetry measurements. In an effort to address this issue, this study proposes a two-step method for photon denoising by utilizing a method combining the DBSCAN algorithm and a two-dimensional window filter, achieving an F1 score of 0.94. A robust M-estimation method was employed to estimate the water depth of the denoised and refraction-corrected bathymetric photons, achieving an RMSE of 0.30 m. The method proposed in this paper preserves as much information as possible about signal photons, increases the number of bathymetric points, enhances the resistance to gross error, and guarantees the accuracy of bathymetry measurements while outlining the underwater topography. While the method is not fully automated and requires setting parameters, the fixed parameter values allow for efficient batch denoising of underwater photon points in different environments.