Aquaculture plays a critical role in food security and nutrition strategies. The application of intelligent aquaculture technology has shown promising performance in improving aquaculture productivity and increasing economic benefits with its rapid advancement and good prospects. However, degraded underwater images have hampered the existing computer vision applications in intelligent aquaculture. To this end, a novel Tied Bilateral learning network is proposed for Aquaculture Image Enhancement (TBAIE), which improves the degraded aquaculture images to meet the requirements of various computer vision applications in aquaculture. Concretely, a novel multiple tied guidance module is designed to generate a multi-channel feature map and capture long-range features based on input. Then, a feature fusion module is introduced with a novel tied attention block to blend the feature and suppress noise with a low computational resource. Experimental results demonstrate that the proposed TBAIE can improve the quality of aquaculture images and remove color distortion. Moreover, TBAIE can achieve state-of-the-art in quantitative and qualitative metrics and meet the practical requirements of different aquaculture vision tasks.