To address the flaws of existing low-light image enhancement methods based on Retinex theory, such as complex training procedures, difficulties in acquiring the ground truths of illumination and reflection components during training, and issues affecting image quality by amplifying dark-region noise and losing structural details when enhancing images under extremely poor lighting conditions, this paper proposes an end-to-end two-stage image enhancement network that combines Retinex theory with diffusion models. In the first stage, guided by Retinex theory, the focus is on improving the brightness of low-light images. A convolutional neural network (CNN) is adopted to estimate the three-channel illumination ratio map, which is then dot-multiplied with the low-light image to obtain the initial enhanced result. Pure Retinex methods barely consider the degradations hidden in dark areas during brightness enhancement. After initially brightening the low-light image, the second stage focuses on denoising and restoring the image using the excellent denoising capability of diffusion models. A brightness-aware diffusion model is proposed, which takes the luminance map in the HSI color space as a condition to fully leverage the advantages of diffusion models in repairing degradations from the initial enhancement. A color correction module is also introduced to mitigate potential global degradation during the inverse process of the diffusion model, yielding the final enhanced image. Experimental results show that compared with 10 other state-of-the-art algorithms on low-light datasets, the proposed method achieves a peak signal-to-noise ratio (PSNR) of 27.517 and a structural similarity index (SSIM) of 0.874 (a near-optimal value), along with an image perception similarity of 0.087-all outperforming the compared methods. The proposed method can well adapt to the distributions of unknown noise and illumination, achieving excellent performance in brightness enhancement, noise removal, and detail preservation, and generating more natural and high-quality enhanced images.