A Light Diffusion-based Zero-Reference Deep Curve Estimation algorithm （LightDiffu-DCE） is proposed to address the uneven distribution of light intensity from multiple sources in low-light images， which often results in the loss of image contour features and unnatural enhancement effects. To improve the model’s generalization capability， a diffusion model grounded in light intensity modeling of light sources is employed to generate training datasets with varied illumination levels. Subsequently， a depth profile estimation network incorporating edge feature fusion is designed to extract richer multi-scale contour and detail features， thereby enhancing the accuracy of light intensity estimation. Furthermore， atmospheric light estimation is integrated to calculate the illumination of different image regions， enabling dynamic fine-tuning of enhancement curves and coefficients for more natural lighting recovery. Experimental evaluations on the challenging ExDark （non-contrast） and LOL （contrast） datasets， utilizing six rigorous metrics， demonstrate the superiority of LightDiffu-DCE. Specifically， on the ExDark dataset， improvements of approximately 8.35%， 6.20%， and 21.83% are achieved in the no-reference metrics NIQE， PIQE， and RISQ， respectively； on the LOL dataset， gains of approximately 12.12%， 4.76%， and 49.89% are observed in the reference-based metrics PSNR， SSIM， and RMSE. These results substantiate that LightDiffu-DCE effectively enhances low-light images， restoring clarity， vividness， and naturalness.