<p>The complex underwater environment often leads to significant image degradation, such as color distortion, low contrast, and poor visibility, which severely impacts the performance of underwater vision tasks. Existing underwater image enhancement (UIE) methods are typically designed for specific degradation conditions and exhibit limited adaptability to varying underwater environments. To address the challenges posed by diverse degradation conditions, we propose an Adaptive Color-Corrected Multicolor Space Enhancement Network (CCMSE-Net). The CCMSE-Net decomposes the UIE task into two stages: color correction and visibility enhancement, corresponding to an adaptive color correction subnetwork (ACC-Net) and a multicolor space enhancement subnetwork (MCSE-Net), respectively. The MCSE-Net achieves multicolor space feature enhancement by applying the multiscale Retinex (MSR) model to the RGB color space and incorporating a feature extraction module (FEM) for the Lab and HSV color spaces. The fusion of multicolor space features is facilitated by the convolutional residual spatial self-attention block (CRSAB), which effectively captures both local details and global context. Experimental results demonstrate that the CCMSE-Net significantly enhances underwater image quality both quantitatively and qualitatively, offering a robust and adaptable solution for diverse underwater environments. Additionally, the enhanced images substantially improve the performance of downstream underwater vision tasks.</p>