To address the challenges of color cast, blurriness, and detail loss in underwater images caused by light absorption and scattering, this study proposes a KFT-GAN for underwater image enhancement. The paper introduces a KFT module that facilitates the efficient transmission of critical features such as color, edges, and texture within the network. By integrating depthwise separable convolution, a lightweight network model is constructed, reducing the parameter count by 60.6% compared to models utilizing conventional convolution, thereby enhancing the model’s learning efficiency. This module enables the generator’s encoding stage to extract essential key features from the input image and transmit these features to the decoding stage through downsampling and skip connections, improving the quality of the reconstructed image. Additionally, this study proposes a hybrid loss function based on perceptual loss principles, emphasizing multiple key attributes of the image to achieve superior visual quality. The proposed model demonstrates excellent performance on both the EUVP and UIEB datasets, achieving PSNR values of 21.384 2 and 18.025 6, and SSIM values of 0.741 3 and 0.688 9, respectively. Qualitative and quantitative comparisons with traditional algorithms and deep learning methods validate the effectiveness and superiority of the proposed model.