Clear and high-quality underwater images are essential for marine applications, including autonomous navigation, ecological monitoring, and infrastructure inspection. However, underwater images typically suffer from severe colour distortion, low contrast, and diminished structural visibility due to wavelength-dependent attenuation, scattering, and uneven illumination conditions. Recent deep learning-based underwater image enhancement (UIE) methods primarily adopt end-to-end frameworks, directly regressing enhanced images from degraded inputs. While these approaches have achieved significant progress, they often lack explicit modeling of the degradation process, leading to limited interpretability and suboptimal recovery of fine-grained details. To address these limitations, we propose DRM-Net, an explicit residual learning framework for UIE. Rather than estimating the enhanced image directly, DRM-Net first predicts a pixel-wise Degradation Residual Map (DRM) in the perceptually uniform CIELab colour space. This map explicitly quantifies local colour, contrast, and structural degradations, thereby enabling the network to precisely reconstruct missing visual information. Furthermore, we design a lightweight Subaquatic Multi-Scale Context Fusion module, which utilizes parallel atrous convolutions with softmax-weighted feature aggregation, significantly enhancing robustness against spatially heterogeneous scattering. Trained jointly with pixel-wise DRM and VGG-based perceptual losses, DRM-Net achieves superior colour fidelity, perceptual realism, and structural detail recovery. Comprehensive experiments conducted on multiple benchmarks demonstrate that our proposed approach attains competitive quantitative results and superior qualitative visual performance compared to state-of-the-art UIE methods, while maintaining low computational overhead, making it particularly suitable for resource-constrained underwater robotic systems.

Rehabilitation therapies are widely employed to assist people with motor impairments in regaining control over their affected body parts. Nevertheless, factors such as fatigue and low self-efficacy can hinder patient compliance during extensive rehabilitation processes. Utilizing hand redirection in virtual reality (VR) enables patients to accomplish seemingly more challenging tasks, thereby bolstering their motivation and confidence. While previous research has investigated user experience and hand redirection among able-bodied people, its effects on motor-impaired people remain unexplored. In this paper, we present a VR rehabilitation application that harnesses hand redirection. Through a user study and semi-structured interviews, we examine the impact of hand redirection on the rehabilitation experiences of people with motor impairments and its potential to enhance their motivation for upper limb rehabilitation. Our findings suggest that patients are not sensitive to hand movement inconsistency, and the majority express interest in incorporating hand redirection into future long-term VR rehabilitation programs.