With the exploration and exploitation of marine resources, underwater images, which serve as crucial carriers of underwater information, significantly influence the advancement of related fields. Despite dozens of underwater image enhancement (UIE) methods being proposed, the impacts of insufficient contrast and distortion of surface texture during UIE are currently underappreciated. To address these challenges, we propose a novel UIE method, channel-adaptive and spatial-fusion Net (CASF-Net), which uses a network channel-adaptive correction module (CACM) to enhance feature extraction and color correction to solve the problem of insufficient contrast. In addition, the CASF-Net utilizes a spatial multi-scale fusion module (SMFM) to solve the surface texture distortion problem and effectively improve underwater image saturation. Furthermore, we propose a Large-scale High-resolution Underwater Image Enhancement Dataset (LHUI), which contains 13,080 pairs of high-resolution images with sufficient diversity for efficient UIE training. Experimental results show that the proposed network design performs well in the UIE task compared with existing methods.