Abstract: To enhance image quality in low-light conditions, an unsupervised dual-path low-light image enhancement algorithm is proposed, integrating color correction and structural information. The algorithm utilizes a generative adversarial network (GAN) with a generator that employs a dual-branch architecture to concurrently handle color and structural details, resulting in natural color restoration and clear texture details. A spatial-discriminative block (SDB) is introduced in the discriminator to improve its judgment capability, leading to more realistic image generation. An illumination-guided color correction block (IGCB) uses illumination features to mitigate noise and artifacts in low-light environments. The selective kernel channel fusion (SKCF) and convolution attention block (CAB) modules enhance the semantic and local details of the image. Experimental results show that the algorithm outperforms classical methods on the LOL and LSRW datasets, achieving PSNR and SSIM scores of 19.89 and 0.672, respectively, on the LOLv1 dataset, and 20.08 and 0.693 on the LOLv2 dataset. Practical applications confirm its effectiveness in restoring brightness, contrast, and color in low-light images.