<p>Due to the attenuation and scattering of underwater light and water conditions, underwater images usually suffer from degradation problems such as color distortion and detail blurring, which seriously affect underwater engineering and research tasks. Previous underwater image enhancement (UIE) methods primarily focus on spatial domain enhancement, neglecting the crucial role of frequency-domain information. This bias leads to distortion of low-frequency background colors and loss of high-frequency texture details. In contrast, frequency-domain analysis effectively separates and enhances components of different frequencies, fundamentally improving the image’s authenticity and detail representation. To compensate for these shortcomings, we propose a frequency-domain attention-guided generative adversarial network (GAN). Our approach contains the following key components: first, we design a frequency-domain attention-guided module (FDAG) for guiding the network to learn key frequency-domain information in the image. Second, to address the problem of high-frequency information loss during feature propagation from the encoder to the decoder, we propose a Frequency Restoration Block (FRB). This unit contains a set of filters that can co-emphasize the medium and high frequencies of the input signal. Finally, we propose the global spatial self-calibrating convolution block (GSSC), which effectively combines global information, local features, and spatial attention to further refine important details in the image. Experiments on benchmark synthetic and real underwater image datasets demonstrate our method. It achieves an average improvement of 2% in PSNR and 2.77% in SSIM compared to the best-performing baseline. Additionally, it exhibits superior visual quality in terms of color restoration and detail preservation. Extensive ablation studies and comparative analyses further validate the effectiveness and robustness of our approach.</p>