In order to address the limitations of existing low-light image enhancement (LLIE) algorithms, such as inadequate utilization of frequency-domain information and insufficient cross-domain interaction between frequency and spatial domains, a LLIE algorithm was proposed based on Fourier convolution and wavelet transform self-attention mechanism, termed F-WNet. The core innovation of the algorithm lied in combining Fourier convolution and wavelet transform self-attention to construct a dual-branch network structure, achieving complementary enhancement of frequency-domain and spatial-domain information at the neck of the encoder-decoder framework. The Fourier convolution branch effectively captured global frequency-domain features through fast Fourier convolution residual blocks. The wavelet transform self-attention branch optimized the self-attention mechanism using wavelet transform, which significantly reduced computational complexity while establishing long-range dependencies, thereby enhancing the modeling capability for local details and regional relationships. The outputs of the two branches were adaptively fused through a channel attention fusion module with dynamic weight allocation, further improving feature representation. Finally, the proposed method was evaluated on seven publicly available LLIE benchmark datasets. Experimental results show that the proposed algorithm effectively enhances the brightness of low-light images, reliably suppresses noise, robustly maintains color accuracy, and demonstrates significant advantages in preserving texture details. F-WNet achieves the best performance on 13 evaluation metrics. On the three paired datasets, it attains peak signal-to-noise ratio (PSNR) of 26.977, 27.794, and 28.592 dB, structural similarity (SSIM) of 0.881, 0.897, and 0.943, and learned perceptual image patch similarity (LPIPS) scores of 0.096, 0.120, and 0.052, respectively. On three unpaired datasets, blind/referenceless image spatial quality evaluator (BRISQUE) scores of 25.84, 20.54, and 22.09 and natural image quality evaluator (NIQE) values of 3.75, 4.15, and 3.91 are achieved, respectively. The proposed feasible Fourier-wavelet joint enhancement algorithm effectively utilizes frequency-domain information, establishing a novel cross-domain interaction mechanism that offers a feasible new solution for low-light image enhancement.