In response to the problem that current plug-and-play image restoration methods cannot accurately model image degradation models in blind image restoration tasks such as low-light image enhancement, this study constructs a solution that combines a plug-and-play splitting algorithm with a guided diffusion model. This solution cleverly avoids directly solving complex data sub-problems caused by complex degradation models. Instead, it uses real degraded images to solve data sub-problems and takes the solutions of data sub-problems as “anchor points” to indirectly constrain and optimize the solving process of prior sub-problems. This ensures that the image restoration results can be more closely approximated to the real image restoration target. This method is validated on multiple public datasets. The results show that the proposed algorithm achieves an average improvement of 4.89% in PSNR and 9.48% in SSIM compared to current representative methods. Experiments prove that the proposed method performs better in repair metrics, validating its effectiveness.