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

Due to the scattering and attenuation of light into the water, the underwater image usually appears with color distortion, blurred details, and low contrast. To address these problems, a novel two-stage underwater image convolutional neural network (CNN) based on structure decomposition (UWCNN-SD) for underwater image enhancement is proposed by considering the characteristics of underwater imaging. Specifically, the raw underwater image is decomposed into high-frequency and low-frequency based on theoretical analysis of the underwater imaging. Then, a two-stage underwater enhancement network including a preliminary enhancement network and a refinement network is proposed. In the first stage, the preliminary enhancement network, which contains the high-frequency and the low-frequency enhancement networks, is proposed. The high-frequency part is enhanced directly by a deep learning network, and the low-frequency enhancement network is based on the underwater imaging, which is integrated transmission map and background light into joint component map. In the second stage, the refinement network is designed to further optimize the color of the underwater image by considering complexity of underwater imaging. The experimental results of synthetic and real-world underwater images/videos demonstrate that the proposed UWCNN-SD method can perform color correction and enhancement on different types of underwater images. The ablation study verifies the effectiveness of each component, and application tests further illustrate that the proposed UWCNN-SD method can obtain underwater images with higher visual quality. The trained model is available at: https://github.com/wushengcong/UWCNN-SD .

In the existing deep learning based watermarking models, extracted image features for fusing with watermark are not abundant enough and more critically, essential features are not highlighted to be learned with the purpose of robust watermarking, both of which limit the watermarking performance. To solve those two drawbacks, this paper proposes an attention-guided robust image watermarking model based on generative adversarial network (ARWGAN). To acquire a great deal of representational image features, a feature fusion module (FFM) is devised to learn shallow and deep features effectively for multi-layer fusion with watermark, and meanwhile, reuse of those features by the dense connection enhances robustness. To alleviate image distortion caused by embedding watermark, an attention module (AM) is deployed to compute the attention mask by mining the global features of the original image. Specifically, with the guidance of the attention mask, image features representing inconspicuous regions and texture regions are enhanced for embedding the high strength of watermark, and simultaneously other features are suppressed to improve the watermarking performance. Furthermore, the noise sub-network is adopted for robustness enhancement by simulating various image attacks in iterative training. The discriminator is used to distinguish the encoded image from the original image for improving watermarking invisibility continuously. Experimental results demonstrate that ARWGAN is superior to the existing state-of-the-art watermarking models, and ablation experiments prove the effectiveness of the FFM and the AM. The code is avaliable in https://github.com/river-huang/ARWGAN.

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.