MobileGAN: A Lightweight Underwater Image Enhancement Framework with Dual-Reference Regularization and Theoretical Analysis

Underwater image enhancement is critical for marine robotic perception, yet existing methods often face a persistent trade-off between restoration quality, structural reliability, and deployment efficiency. Although lightweight enhancement networks are attractive for resource-constrained underwater platforms, many of them mainly rely on empirical architectural simplification and appearance-oriented objectives, with limited mathematical analysis of complexity reduction, semantic regularization, and optimization coordination. To address this issue, this paper proposes MobileGAN, a lightweight underwater image enhancement framework equipped with dual-reference regularization and a theoretical analysis module. The proposed generator adopts a compact encoder–bottleneck–decoder architecture based on depthwise separable convolutions, which substantially reduces convolutional redundancy while preserving effective restoration capability. A dual-reference feature consistency formulation is introduced to simultaneously constrain the enhanced image toward the high-quality target representation and the degraded-input semantic anchor. In addition, an edge-aware regularization term and a stage-wise dynamic weighting mechanism are incorporated to improve local structure recovery and multi-objective optimization behavior. Beyond architectural design, we provide a mathematical analysis of the proposed framework from three aspects: computational complexity reduction, geometric interpretation of dual-reference regularization, and piecewise optimization properties of stage-wise weighted training. Extensive experiments on the UIEB benchmark demonstrate that MobileGAN achieves a favorable trade-off between enhancement quality and computational efficiency. The proposed method maintains real-time inference with a compact model size while providing competitive structural consistency and detail restoration. These results indicate that MobileGAN is not only a practical deployment-oriented enhancement framework but also an interpretable optimization model with analyzable structural properties.