Vision is an important source of information for underwater observations, but underwater images commonly suffer severe visual degradation due to the complexity of the underwater imaging environment and wavelength-dependent absorption effects. There is an urgent need for underwater image enhancement techniques to improve the visual quality of underwater images. Due to the scarcity of high-quality paired training samples, underwater image enhancement based on deep learning has never achieved success similar to other vision tasks. Instead of learning complicated distortion-to-clear mappings with deep networks, we design a template-free color transfer learning framework for predicting transfer parameters, which are more easily captured and described. In addition, we add attention-driven modules to learn differentiated transfer parameters for more flexible and robust enhancement. We verify the effectiveness of our method on multiple publicly available datasets and show its efficiency in enhancing high-resolution images. The source code and the trained models are available on the project homepage: https://trentqq.github.io/TCTL-Net.html.

Estimating depth from a single underwater image is one of the main tasks of underwater visual perception. However, data-driven underwater depth estimation methods have long been challenging to make breakthroughs due to the difficulty of obtaining a large number of true-value references. This is partly due to the high cost of acquisition equipment, which is difficult to be applied to diverse ocean scenes by a wide range of users, and therefore sample diversity is difficult to guarantee; on the other hand, manual annotation of dense depth relationships is almost impossible to achieve. In this paper, we establish a new underwater relative depth estimation benchmark, namely SUIM-SDA, by extending the SUIM dataset with more than 6,000 manually annotated depth trendlines, 25 million pixels with paired depth-ranking labels and 14 million depth-ranked pixel pairs. Using the sparse depth relation annotation provided by SUIM-SDA and the semantic information provided by SUIM, we design a new multi-stage multi-task learning framework to predict a dense relative depth map for a single underwater image. Comprehensive comparison and ablation study on the publicly available dataset and our new benchmark demonstrate the effectiveness of the proposed weakly-supervised strategy for dense relative depth estimation. The new benchmark, source code, and trained models are available on the project home page: https://wangxy97.github.io/WsUIDNet.

Due to the wavelength-dependent light absorption and scattering, the raw underwater images are usually inevitably degraded. Underwater image enhancement (UIE) is of great importance for underwater observation and operation. Data-driven methods, such as deep learning-based UIE approaches, tend to be more applicable to real underwater scenarios. However, the training of deep models is limited by the extreme scarcity of underwater images with enhancement references, resulting in their poor performance in dynamic and diverse underwater scenes. As an alternative, enhancement reference achieved by volunteer voting alleviate the sample shortage to some extent. Since such artificially acquired references are not veritable ground truth, they are far from complete and accurate to provide correct and rich supervision for the enhancement model training. Beyond training with single reference, we propose the first comparative learning framework for UIE problem, namely CLUIE-Net, to learn from multiple candidates of enhancement reference. This new strategy also supports semi-supervised learning mode. Besides, we propose a regional quality-superiority discriminative network (RQSD-Net) as an embedded quality discriminator for the CLUIE-Net. Comprehensive experiments demonstrate the effectiveness of RQSD-Net and the comparative learning strategy for UIE problem. The code, models and new dataset RQSD-UI are available at: https://justwj.github.io/CLUIE-Net.html/.

Abstract Due to the inevitable wavelength‐dependent light absorption and forward/backward scattering, underwater images usually suffer severe color distortion and are hazy. It has become quite necessary to improve the visual quality of underwater images for both underwater observation and operation. Traditional enhancement methods and existing deep learning‐based approaches to underwater image enhancement usually produce unsatisfactory results for photographs taken in complicated, wild underwater scenes. In such scenes, complex and diverse degradation‐enhancement mappings are often difficult to model, especially since there are very limited samples available for learning. Inspired by the success of color‐transfer techniques, it is found that clear template image‐assisted color transfer is a promising strategy for underwater image enhancement, including not only color correction but also contrast and visibility improvement. Therefore, instead of directly learning the complex deep enhancement models, it is proposed to select proper color‐transfer templates by learning the latent consistency between the templates and the raw underwater images. The proposed new enhancement strategy alleviates the problem caused by incomplete color‐correction models and provides more stable enhancements by utilizing color transfer with consideration of global color distribution consistency and local visual contrast. Comprehensive experiments conducted on UIEB, RUIE, URPC and SQUID datasets demonstrate the good performance and great potential of the proposed new underwater image enhancement strategy.

In underwater scenes, degraded underwater images caused by wavelength-dependent light absorption and scattering present huge challenges to vision tasks. Underwater image enhancement has attracted much attention due to the significance of vision-based applications in marine engineering and underwater robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, almost all existing approaches focus only on the enhancement of independent images. Considering that images photographed in the same underwater scene usually share similar degradation, related images can provide rich complementary information for each other’s enhancement. In this paper, we propose an Underwater Image Co-enhancement Network (UICoE-Net) based on an encoder-decoder Siamese architecture. For joint learning, we introduced correlation feature matching units into the multiple layers of our Siamese encoder-decoder structure in order to communicate the mutual correlation of the two branches. Extensive experiments using the Underwater Image Enhancement Benchmark (UIEB), Underwater Image Co-enhancement Dataset (UICoD) collected from an underwater video dataset with ground-truth reference and Stereo Quantitative Underwater Image Dataset (SQUID) dataset demonstrate the effectiveness of our method.

Due to the wavelength-dependent light attenuation, refraction and scattering, underwater images usually suffer from color distortion and blurred details. However, due to the limited number of paired underwater images with undistorted images as reference, training deep enhancement models for diverse degradation types is quite difficult. To boost the performance of data-driven approaches, it is essential to establish more effective learning mechanisms that mine richer supervised information from limited training sample resources. In this paper, we propose a novel underwater image enhancement network, called SGUIE-Net, in which we introduce semantic information as high-level guidance via region-wise enhancement feature learning. Accordingly, we propose semantic region-wise enhancement module to better learn local enhancement features for semantic regions with multi-scale perception. After using them as complementary features and feeding them to the main branch, which extracts the global enhancement features on the original image scale, the fused features bring semantically consistent and visually superior enhancements. Extensive experiments on the publicly available datasets and our proposed dataset demonstrate the impressive performance of SGUIE-Net. The code and proposed dataset are available at https://trentqq.github.io/SGUIE-Net.html.