Underwater images often suffer from significant degradation due to light refraction and absorption in water, which can make it difficult to see clearly. Current methods for improving these images usually focus on color, brightness, and contrast, but often overlook the important role of phase information in enhancing clarity and detail. To tackle these challenges, we have developed PEUTNet, a new model for enhancing underwater images that takes phase information into account. Our model uses phase data from grayscale and RGB images during feature extraction to capture edges and details more effectively. Additionally, we improve phase transmission from the encoder to the decoder. This approach allows PEUTNet to outperform existing methods in terms of both visual appearance and quantitative measures of quality.