Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement performance is susceptible to the quality of the reference images, which is more pronounced in underwater image enhancement tasks because the ground truths are not available. In this paper, we propose a light-enhanced pixel fusion network (LEPF-Net) to solve this problem. Specifically, we first introduce a novel light enhancement block (LEB) based on the residual block (RB) and the light enhancement curve (LE-Curve) to restore the cast color of the images. The RB is adopted to learn and obtain the feature maps from an original input image, and the LE-Curve is used to renovate the color cast of the learned images. To realize the superb detail of the repaired images, which is superior to the reference images, we develop a pixel fusion subnetwork (PF-SubNet) that adopts a pixel attention mechanism (PAM) to eliminate noise from the underwater image. The PAM adapts weight allocation to different levels of a feature map, which leads to an enhancement in the visibility of severely degraded areas. The experimental results show that the proposed LEPF-Net outperforms most of the existing underwater image enhancement methods. Furthermore, among the five classic no-reference image quality assessment (NRIQA) indicators, the enhanced images obtained by LEPF-Net are of higher quality than the ground truths from the UIEB dataset.

Existing multi-color space guided techniques for underwater image enhancement (UIE) fail to take the advantages of the XYZ color space for preserving underwater image details, meanwhile, existing UIE datasets, typically containing low-quality reference images of distorted colors and blurred structures, lead to inaccurate enhancement mapping between low-quality and high-quality images. To overcome these above limitations, we propose a Multi-Color Space Fusion Network (MCSF-Net) for UIE. The MCSF-Net incorporates a Multi-dimensional Feature Fusion Block (MFFB) and weighted feature fusion scheme to effectively integrate complementary features from both XYZ and RGB color spaces. Moreover, we establish a Large-Scale Mixed UIE dataset (LSMU) by using nine no-reference metrics to filter out low-quality reference images from eight public UIE datasets, enabling more effective network learning. Extensive experiments on mainstream datasets demonstrate that the proposed method outperforms several leading approaches in both color restoration and detail enhancement of various underwater images. The code and dataset for MCSF-Net will be available at https://github.com/WYJGR/MCSF-Net.

Background: Corvis ST (CVS) provides dynamic corneal response parameters for biomechanical assessment, but conventional metrics may not uniquely reflect region-specific alterations in corneal material properties. This study investigated how regional changes in corneal material properties influence CVS biomechanical parameters and explored a multi-parameter strategy for regional mechanical interpretation. Methods: An idealized finite element corneal model was divided into central, paracentral, and peripheral zones, and the stiffness of each zone was independently adjusted to 70–130% of baseline to simulate regional softening or stiffening. An air-puff load was applied to the anterior corneal surface, and nodal coordinates were used to calculate conventional CVS parameters and the newly proposed DA Ratio X series. Normalized relative changes and univariate linear regression were used to characterize response direction and sensitivity. Results: Most parameters showed approximately linear responses to regional stiffness alterations, with region-dependent differences in response direction and magnitude. Central material changes produced the largest responses. Similar parameter values or trends could arise from different regional mechanical alterations, indicating that a single parameter may not uniquely identify the underlying regional change. Within this idealized simulation framework, the extended DA Ratio series improved regional differentiation, and the combination of DeflArea, DAR4, and IIR provided a candidate theoretical framework for distinguishing central, paracentral, and peripheral changes. Conclusions: CVS biomechanical parameters were influenced by the spatial location of corneal material alterations, and single-parameter interpretation may not uniquely reflect the underlying regional mechanical changes. Coordinated interpretation of multiple parameters demonstrated distinct response patterns across different modeled regions and may provide ideas for future regional biomechanical assessment. Further validation using patient-specific models and clinical datasets is warranted to evaluate the applicability of this framework.

Underwater Monocular Depth Estimation (UMDE) is a critical task that aims to estimate high-precision depth maps from underwater degraded images caused by light absorption and scattering effects in marine environments. Recently, Mamba-based methods have achieved promising performance across various vision tasks; however, they struggle with the UMDE task because their inflexible state scanning strategies fail to model the structural features of underwater images effectively. Meanwhile, existing UMDE datasets usually contain unreliable depth labels, leading to incorrect object-depth relationships between underwater images and their corresponding depth maps. To overcome these limitations, we develop a novel tree-aware Mamba method, dubbed Tree-Mamba, for estimating accurate monocular depth maps from underwater degraded images. Specifically, we propose a tree-aware scanning strategy that adaptively constructs a minimum spanning tree based on feature similarity. The spatial topological features among the tree nodes are then flexibly aggregated through bottom-up and top-down traversals, enabling stronger multi-scale feature representation capabilities. Moreover, we construct an underwater depth estimation benchmark (called BlueDepth), which consists of 38,162 underwater image pairs with reliable depth labels. This benchmark serves as a foundational dataset for training existing deep learning-based UMDE methods to learn accurate object-depth relationships. Extensive experiments demonstrate the superiority of the proposed Tree-Mamba over several leading methods in both qualitative results and quantitative evaluations with competitive computational efficiency. Code and dataset will be available at https://wyjgr.github.io/Tree-Mamba.html.