To address the issue of degraded image quality in low-dose X-ray computed tomography (LDCT) caused by significantly reduced ionizing radiation doses in current denoising methods, this paper proposes an enhanced diffusion model based on the U-Net network. The model introduces an error modulation module to resolve the problem of error accumulation during sampling. Additionally, a composite loss function that combines L1, adversarial, and self-supervised multiscale perceptual losses is proposed. This composite loss function is designed to simultaneously suppress image noise and preserve details, thereby overcoming the oversmoothing phenomenon. Experimental results demonstrate that the proposed algorithm achieves an improvement of 1.36 dB in peak signal-to-noise ratio and 0.02 in structural similarity index, effectively suppressing noise while better preserving image details.