Underwater image enhancement technology plays a critical role in marine exploration and robotic operations. However, due to the absorption and scattering of light in water, underwater images commonly suffer from color distortion, low contrast, and loss of detail. This paper proposes an adaptive enhancement network based on lab color space enhancement and attention mechanisms, which decouples and separately processes luminance and chrominance information in the lab color space, enhancing them using different approaches. Furthermore, we design an improved attention module that integrates a 3D convolution-based multi-head self-attention mechanism with depthwise separable convolutions, effectively capturing long-range dependencies while preserving local features. Experimental results demonstrate that our method performs excellently in complex underwater conditions, exhibits strong cross-scenario consistency, and produces natural and realistic images. The proposed solution significantly outperforms existing methods in enhancing image details and effectively addresses issues such as color distortion, uneven illumination, and detail loss, providing an innovative approach to underwater image enhancement.