<p>Vascular contrast enhancement is crucial for early disease diagnosis and surgical precision in robotic surgery imaging. Traditional white-light imaging often fails to distinguish blood vessels due to the spectral similarity between vessels and surrounding tissues. Although techniques like narrow-band imaging improve contrast, they require specialized hardware and exhibit inconsistent performance across different surgical environments. To address these limitations, we propose StarVasc, a novel lightweight framework for unsupervised vascular contrast enhancement tailored for robotic surgical vision systems. StarVasc leverages an unpaired learning strategy based on a compact generative adversarial network. The generator incorporates a star operation module, enabling hyper-dimensional feature expansion. This operation implicitly maps input images into an exponentially high-dimensional nonlinear feature space, facilitating efficient representation of fine-grained vascular structures without increasing model complexity. In addition, we design a Spectral Feature Enhancement Module (SFEM) to further refine vascular detail. Acting as a narrow-band feature extractor, SFEM implicitly learns spectral cues without requiring hyperspectral input. It operates in a self-supervised reconstruction paradigm, ensuring that the extracted features are semantically aligned with vascular structures. Integrated within an encoder-decoder architecture, SFEM enhances vessel clarity and edge continuity in the output images. Extensive experiments demonstrate that StarVasc consistently outperforms both traditional enhancement techniques and recent deep learning methods across no-reference quality metrics and visual evaluations. Without relying on specialized hardware, StarVasc provides an adaptive, clinically viable solution for real-time vascular enhancement in robotic surgical imaging, contributing to improved visual perception and surgical safety in automated or robot-assisted interventions.</p>