Single underwater image often suffer from severe quality degradation and field-of-view limitation due to the underwater light propagation characteristics and the viewing range of camera equipment. To address these challenges, we propose a underwater scene clarity reconstruction framework called USCR, which comprises a multilayer information fusion (MIF) method for underwater image enhancement (UIE) and a self-organized stitching (SOS) method for image stitching. First, MIF corrects color distortion, enhances contrast, and highlights image detail information through a minimally attenuated channel guided color correction strategy and a gradient weight fusion strategy. Subsequently, SOS is applied to stitch the enhanced underwater images, which utilizes a homography matrix to initially stitch the image sequence, and further employs a pixel blending strategy based on boundary distance weighting for boundary pixel fusion to the initial stitch image, aiming to ensure a homogeneous transition of the stitch region. Our reconstructed underwater scenes are characterized by visual clarity and a wide field-of-view. Extensive qualitative and quantitative experimental validations show that USCR outperforms the state-of-the-art methods in underwater visual reconstruction task.

Underwater image captioning bridges the gap between visual perception and semantic understanding of underwater scenes, playing a crucial role in applications such as ocean geoscience and underwater remote sensing. Despite progress in this field, limitations remain in achieving accurate underwater image captioning. The main limitations are: (a) the underestimation of basic sketch features in underwater image captioning, and (b) insufficient consideration of the impact of scale differences in underwater objects. To overcome these limitations, we propose underwater image captioning with AquaSketch enhanced cross-scale information fusion. Our novel contributions are twofold: (a) A novel AquaSketch (i.e., aqua sketch) enhancement method is developed to reduce the impact of underwater image distortion on scene understanding, while enhancing both detailed and background information; and (b) A top-down dual-branch pyramid for cross-scale information fusion is proposed. This architecture fuses multi-scale feature information from two branches through an attention-based feature fusion structure, performing cross-scale fusion in a top-down manner. The resulting pyramid fusion features offer a comprehensive representation of underwater object information. Collectively, these contributions facilitate the generation of accurate and comprehensive underwater image captions. Experimental evaluations on three datasets demonstrate that our proposed underwater image captioning model achieves state-of-the-art performance in the field.

The underwater color disparity is an important cue for enhancing an underwater image. Applying the underwater color disparity indiscriminately to the entire underwater image tends to give rise to foreground-background crosstalk with either excessive foreground or insufficient background enhancement. To address the discriminativeness between underwater color disparities in foreground and background regions, we develop a discriminative underwater image enhancement method empowered by large foundation model technology. We first utilize the Segment Anything Model to generate segmentation masks, dividing the underwater image into foreground and background regions. This enables accurate foreground-background separation. Then, we conduct adaptive color compensation and fusion to improve the color histogram similarity for foreground and background regions separately. This corrects color deviations and improves contrasts in a discriminative manner that avoids the foreground-background crosstalk. Finally, we propose high-frequency edge fusion to extract high-frequency components from both the original underwater image and the fused image, and then fuse these components to obtain the final enhanced image. This eliminates blurred details arising from the discriminative processing of foreground and background regions. Our method represents the pioneering application of large foundation model technology to empower underwater image enhancement. Experimental results indicate that our method outperforms nine state-of-the-art underwater image enhancement methods in visual quality, achieves superior results across five underwater image quality evaluation metrics on three underwater image datasets, and is beneficial for practical applications such as underwater feature matching. We release our code at https://gitee.com/wanghaoupc/UIE SAM.

One of the main challenges facing hyperspectral image super-resolution is the complex high dimensional data processing. Mamba leverages its ability to model long-range dependencies of linear complexity to capture the global spatial and spectral information of high-dimensional data while maintaining linear complexity. However, its visual state space equation mainly focuses on the band dimension mapping of the image, while ignoring the modeling of the spatial dimension. To overcome this limitation, we develop a Mamba hyperspectral image super-resolution framework, which comprises three essential components. The first component, i.e., spatial Mamba sub-network, models the spatial dimensions of hyperspectral data. It captures long-range dependencies in the pixel space, thereby integrating global spatial information into the framework. The second component, i.e., spectral Mamba sub-network, serves to capture long-range spectral dependencies. The third component, i.e., reconstruction, generates hyperspectral images with rich spatial and spectral details through pixel interpolation. Our Mamba framework fully develops the potential of the Mamba model in hyperspectral image super-resolution, significantly enhancing the restoration quality and accuracy of hyperspectral images. Extensive experiments on the Houston and QUST-1 datasets show that our framework outperforms state-of-the-art methods in both quantitative metrics and visual quality across diverse scenarios. We release our source code at https://gitee.com/xu_yinghao/MambaHSISR for public evaluations.

Underwater images often reveal color distortion and poor visibility due to light propagation in water being affected by the selective absorption and scattering of suspended particles. This letter presents an efficient color channel transfer (CCT) method that largely restores color distortion and improves visibility of underwater images. Any captured underwater image with at least one color channel is highly attenuated in real underwater imaging. To compensate for the loss of information in the attenuated channel, the CCT transfers the degraded image to the CIELab color space and compensates for the loss of information in the degraded image by adjusting luminance and chrominance. The reference values of the color transfer image are statistically calculated from many high-quality images to ensure a relatively balanced color distribution. Extensive experiments on three underwater image datasets show that after applying our CCT, the enhancement method leads to satisfactory results in both metric scores and runtimes.

We observe that a natural image tends to exhibit similar histograms for color channels in the RGB color space and consistent statistical estimates for color channels in the Lab color space. We refer to these observations as natural color consistencies. In contrast, we discover that an underwater image does not always follow the natural color consistencies. Different color channels in an underwater image tend to give rise to very different distributions, regardless of whether the channels are in the RGB color space or Lab color space. We refer to these observations as underwater color disparities. To enhance an underwater image to make it appear more natural, it is necessary to correct its underwater color disparities to align with the natural color consistencies. To this end, we develop an adaptive attenuated channel compensation method based on optimal channel precorrection and a salient absorption map-guided fusion method for eliminating the color deviation in the RGB color space. We then develop a method to enhance the contrast of channel L and an adaptive color distribution specification method for improving the contrast and matching the color distribution in the Lab color space. Additionally, we develop an edge-enhanced mask fusion method for correcting blurry details. Our method is not a deep learning method but can effectively be applied to a single underwater image. The qualitative and quantitative empirical results validate that our method outperforms state-of-the-art underwater image enhancement methods. We release the reproducible code athttps://gitee.com/wanghaoupc/Underwater_Color_Disparitiesfor public evaluation.

Underwater images normally suffer from visual degradation issues such as color deviations, low contrasts, and blurred details. Recently, numerous underwater image enhancement algorithms have been proposed to address these issues. However, constrained by underwater conditions, acquiring non-underwater images and depth maps for underwater images is often challenging. This limitation significantly hampers the performance of data driven-based methods and physical model-based methods. Additionally, existing physical model-based methods typically require manual parameter settings, which tend to be bruteforce and insufficient to effectively address the diverse underwater scenes. To overcome these limitations, this paper presents a comprehensive underwater image enhancement framework comprising three phases: metamergence (i.e., meta submergence), metalief (i.e., meta relief), and metaebb (i.e., meta ebb). These phases are dedicated to virtual underwater image synthesis, underwater image depth map estimation, and the configuration of state-of-the-art physical models for underwater image enhancement by reinforcement learning, separately. While the three phases are trained separately, the former phase provides the necessary data for training the latter. We refer to the overall three phases as metalantis (i.e., meta Atlantis) because its training processes, involving variations from submergence via relief to ebb over indoor scenes, mimic the virtual variations of Atlantis. The metalantis framework empowers state-of-the-art physical models of underwater imaging through reinforcement learning with virtually generated data. The well-trained metalantis framework can take an underwater image as the sole input, process it into virtual representations, and finally enhance it. Comprehensive qualitative and quantitative empirical evaluations validate that our metalantis framework outperforms state-of-the-art underwater image enhancement methods. We release our code at https://gitee.com/wanghaoupc/Metalantis_UIE.