To correct the color distortion and enhance the details of degraded underwater image， this paper proposes a coarse-to-fine underwater image enhancement method based on multi-level wavelet transform. Firstly， a raw underwater image is decomposed into a low-frequency image and a series of high-frequency images based on the wavelet transform. Secondly， a two-stage underwater enhancement network is proposed， which includes a multi-level wavelet transform sub-network and a refinement sub-network with the proposed second-order Runge-Kutta block. The multi-level wavelet transform sub-network， which estimates preliminary result， contains a low-frequency and a high-frequency branch. Specifically， the low-frequency branch treats the color correction problem as the implicit style transfer problem and introduces the instance normalization and the position normalization into the branch. To ensure an accurate reconstruction， when manipulating low-frequency information， the high-frequency branch calculates the enhanced mask according to the information from both low- and high-frequency images and implements the enhancement by multiplying the progressive up-sampling enhanced mask with the high-frequency images. We implemented the inverse wavelet transform and obtain the preliminary results. Finally， the refinement network was designed to further optimize the preliminary results with the proposed second-order Runge-Kutta block. Experimental results demonstrated that the proposed method outperformed the existing methods in enhancement effect on both synthetic and real images， whilst the Peak Signal-to-Noise Ratio （PSNR） improved by 9%. The proposed method also meets the requirement of underwater vision tasks， such as color correction， details enhancement， and clarity.