Abstract Underwater images play an increasingly important role in scientific research and industrial fields such as marine military, marine environmental protection, and marine engineering. However, owing to nonuniform lighting conditions, the quality of underwater imaging is often degraded by remarkable color distortion and detail loss. Although existing traditional underwater image enhancement methods have advanced, they are still limited by scarce and low-quality samples, making it difficult to achieve satisfactory results. In this study, a novel network based on the structure-guided former is proposed to effectively address the challenges of color correction and illumination enhancement in underwater images. The proposed cross-axial compression transformer block preserves the powerful global modeling capacity of transformers while significantly enhancing local feature extraction. In addition, the introduction of structural prior information not only guides the feature reconstruction process in the decoder but also effectively corrects color casts and enhances high-frequency details. Comparative experiments and ablation experiments on publicly available datasets have validated the effectiveness of the proposed method.
Chen et al. (Sat,) studied this question.