Aiming at the degradation of underwater images due to light propagation attenuation, scattering, and dissolved suspended matter, a novel underwater image enhancement network integrating a spatial-wise refined feature Transformer (SRFT) module and a channel-wise feature attention enhanced reconstructed Transformer (CFART) module was proposed. Serialization processing and positional encoding on underwater image sequences were performed by the SRFT module and four-layered spatial self-attention mechanisms were applied to capture global degradation differences while establishing long-range feature dependencies. In the CFART module, features were projected via heterogeneous convolutional kernels and then fed into a multi-head self-attention module through channel fusion. And feature information was reconstructed using multilayer perceptron layers with residual connections. Experimental results showed that the proposed algorithm effectively improved issues such as color shift, blurriness, and low contrast issues in underwater images. In terms of objective evaluation metrics, the proposed algorithm outperformed similar methods with MSE of 253.558, PSNR of 25.421, Entropy of 7.488, and UIQM of 4.461. It also demonstrated significant advantages in SSIM and UCIQE tests with SSIM of 0.893 and UCIQE of 0.592. Subjective visual assessments further confirmed that the proposed algorithm provided excellent color correction and detail restoration capabilities for degraded underwater images. By optimizing the quality of underwater images, the proposed algorithm helps to enhance the precision and efficiency of seabed investigations and assessments.