Recently, learning-based algorithms have shown impressive performance in underwater image enhancement. Most of them resort to training on synthetic data and obtain outstanding performance. However, these deep methods ignore the significant domain gap between the synthetic and real data (i.e., inter-domain gap), and thus the models trained on synthetic data often fail to generalize well to real-world underwater scenarios. Moreover, the complex and changeable underwater environment also causes a great distribution gap among the real data itself (i.e., intra-domain gap). However, almost no research focuses on this problem and thus their techniques often produce visually unpleasing artifacts and color distortions on various real images. Motivated by these observations, we propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to simultaneously minimize the inter-domain and intra-domain gap. Concretely, in the first phase, a new triple-alignment network is designed, including a translation part for enhancing realism of input images, followed by a task-oriented enhancement part. With performing image-level, feature-level and output-level adaptation in these two parts through jointly adversarial learning, the network can better build invariance across domains and thus bridging the inter-domain gap. In the second phase, an easy-hard classification of real data according to the assessed quality of enhanced images is performed, in which a new rank-based underwater quality assessment method is embedded. By leveraging implicit quality information learned from rankings, this method can more accurately assess the perceptual quality of enhanced images. Using pseudo labels from the easy part, an easy-hard adaptation technique is then conducted to effectively decrease the intra-domain gap between easy and hard samples. Extensive experimental results demonstrate that the proposed TUDA is significantly superior to existing works in terms of both visual quality and quantitative metrics.

Underwater Image Enhancement (UIE) technology aims to tackle the challenge of restoring the degraded underwater images due to light absorption and scattering. Meanwhile, the ever-increasing requirement for higher resolution images from a lower resolution in the underwater domain cannot be overlooked. To address these problems, a novel U-Net based Reinforced Swin-Convs Transformer for Simultaneous Enhancement and Super-resolution (URSCT-SESR) method is proposed. Specifically, with the deficiency of U-Net based on pure convolutions, the Swin Transformer is embedded into U-Net for improving the ability to capture the global dependency. Then, given the inadequacy of the Swin Transformer capturing the local attention, the reintroduction of convolutions may capture more local attention. Thus, an ingenious manner is presented for the fusion of convolutions and the core attention mechanism to build a Reinforced Swin-Convs Transformer Block (RSCTB) for capturing more local attention, which is reinforced in the channel and the spatial attention of the Swin Transformer. Finally, experimental results on available datasets demonstrate that the proposed URSCT-SESR achieves state-of-the-art performance compared with other methods in terms of both subjective and objective evaluations. The code is publicly available at: https://github.com/TingdiRen/ URSCT-SESR.

Underwater imaging is often affected by light attenuation and scattering in water, leading to degraded visual quality such as color distortion, reduced contrast, and noise. Existing underwater image enhancement (UIE) methods often lack generalization capabilities, making them unable to adapt to various underwater images captured in different aquatic environments and lighting conditions. To address these challenges, a UIE method based on conditional denoising diffusion probabilistic model (DDPM) is proposed (DiffWater), which leverages the advantages of DDPM, and trains a stable and well-converged model capable of generating high-quality and diverse samples. Considering the multiple distortion issues in underwater imaging, unconditional DDPM may not achieve satisfactory enhancement and restoration results. Therefore, DiffWater utilizes the degraded underwater image with added color compensation as a conditional guide, through which the DiffWater achieves highquality restoration of degraded underwater images. Particularly, the proposed DiffWater introduces a color compensation method that performs channel-wise color compensation in the RGB color space, tailored to different water conditions and lighting scenarios, and utilizes this condition to guide the denoising process. In the experimental section, the proposed DiffWater method is tested on four real underwater image datasets and compared against existing methods. Experimental results demonstrate that DiffWater outperforms existing comparison methods in terms of enhancement quality and effectiveness, exhibiting stronger generalization capabilities and robustness

To achieve higher coding efficiency, the latest Versatile Video Coding (VVC) standard adopts a series of new intra coding techniques, including the quadtree plus multi-type tree (QTMT), intra sub-partitions (ISP) and intra block copy (IBC). However, this makes the intra coding more complicated, as VVC needs to traverse all prediction modes and partition types of QTMT to find the optimal combination. In this paper, we propose a fast algorithm for VVC from two aspects of mode selection and prediction terminating to reduce coding complexity. For the mode selection, adaptive mode pruning (AMP) is proposed to remove non-promising modes. First, since the newly introduced modes (IBC and ISP) are not effective for all blocks, learning-based classifiers are designed to remove them intelligently. Second, for normal modes, an ensemble decision strategy is proposed to sort the candidate modes and increase the probability of being the optimal mode for the first few candidates; thus, we can remove redundant candidates more efficiently. In terms of prediction terminating, we find that different optimal modes of current depth level lead to different termination probabilities of remaining intra predictions. Therefore, mode-dependent termination (MDT) is proposed to select an appropriate model through the optimal mode and terminate unnecessary intra predictions of remaining depth levels. The proposed algorithm is implemented on VVC test model, and simulation results show that it can achieve 51% $\sim$ 53% time savings with only 0.93% $\sim$ 1.08% BDBR increases.

Due to the light scattering and absorption of impurities, the quality of underwater imaging is poor, which seriously affects underwater exploration and research. To address the problem, a novel underwater image enhancement method integrating the convolutional neural network (CNN) with a feature fusion Transformer (UIE-Convformer) is proposed. Specifically, the proposed UIE-Convformer adopts a multi-scale U-Net structure to fully mine rich texture information and semantic information. Firstly, considering that CNN is more efficient and comprehensive in extracting local feature information of underwater images, the ConvBlock module based on CNN is proposed to extract local features of images and ensure the efficiency and integrity of feature extraction. Furthermore, considering the serious color deviation caused by the absorption and scattering of light in water, as well as the large-scale blur and diffusion effects in the underwater environment, the feature fusion transformer module (Feaformer) for global information fusion and reconstruction is proposed to establish long-distance feature dependency. Additionally, the Jump Fusion Connection Module (JFCM) is built between the encoder and the decoder to fuse multi-scale features through effective bidirectional cross-connection and weighted fusion, which helps to provide richer feature information for the reconstruction of the decoder. Finally, the refinement module is designed to further optimize the details of the underwater images and achieve better visual effects. Experimental results on available datasets show the effectiveness of the proposed UIE-Convformer.

image can be represented with different formats, such as the equirectangular projection (ERP) image, viewport images or spherical image, for its different processing procedures and applications. Accordingly, the 360-degree image quality assessment (360-IQA) can be performed on these different formats. However, the performance of 360-IQA with the ERP image is not equivalent with those with the viewport images or spherical image due to the over-sampling and the resulted obvious geometric distortion of ERP image. This imbalance problem brings challenge to ERP image based applications, such as 360-degree image/video compression and assessment. In this paper, we propose a new blind 360-IQA framework to handle this imbalance problem. In the proposed framework, cubemap projection (CMP) with six inter-related faces is used to realize the omnidirectional viewing of 360-degree image. A multi-distortions visual attention quality dataset for 360-degree images is firstly established as the benchmark to analyze the performance of objective 360-IQA methods. Then, the perception-driven blind 360-IQA framework is proposed based on six cubemap faces of CMP for 360-degree image, in which human attention behavior is taken into account to improve the effectiveness of the proposed framework. The cubemap quality feature subset of CMP image is first obtained, and additionally, attention feature matrices and subsets are also calculated to describe the human visual behavior. Experimental results show that the proposed framework achieves superior performances compared with state-of-the-art IQA methods, and the cross dataset validation also verifies the effectiveness of the proposed framework. In addition, the proposed framework can also be combined with new quality feature extraction method to further improve the performance of 360-IQA. All of these demonstrate that the proposed framework is effective in 360-IQA and has a good potential for future applications.