Addressing challenges such as severe noise， color deviation， and artifacts in low-light imaging， a novel three-stage low-light image enhancement algorithm based on illumination guidance （IG-TSNet） is proposed. This algorithm synergistically integrates the Fourier domain's capability to capture global image information with the Transformer's strength in modeling long-range dependencies within the spatial domain， utilizing illumination guidance to ensure coherent enhancement. IG-TSNet comprises three sequential stages. In the pixel-wise enhancement stage， an adaptive parameter adjustment mechanism is introduced to improve the global representation of the image. During the Fourier reconstruction stage， illumination priors are employed to optimize both amplitude and phase spectra across two channels following Fourier transformation， enabling comprehensive global image reconstruction. In the cross-attention fusion stage， a lightweight dual-path U-shaped network， incorporating a cross-attention fusion module， is designed to dynamically align Fourier-reconstructed features with illumination-guidance maps. The proposed IG-TSNet was rigorously evaluated on six benchmark datasets for low-light image enhancement， demonstrating superior performance. Qualitative results confirm that the method effectively enhances underexposed regions， suppresses noise without introducing artifacts or patchiness， and preserves color fidelity robustly. Quantitative assessments reveal that IG-TSNet achieves state-of-the-art results across nine evaluation metrics. On three paired datasets， PSNR values of 26.968 dB， 27.880 dB， and 28.939 dB； SSIM values of 0.867， 0.882， and 0.947； and LPIPS values of 0.099， 0.141， and 0.047 were attained， respectively. On three unpaired datasets， BRISQUE scores of 25.67， 20.51， and 18.80 and NIQE values of 3.79， 4.09， and 4.02 were achieved， respectively. This study offers a viable frequency-spatial joint enhancement framework， advancing the field of low-light image enhancement.