为解决低光照条件下的图像噪声多、亮度低、细节模糊等问题，提出了一种融合零参考深度曲线的低照度图像增强与去噪算法（UMDCEAD-NET）。该算法首先设计了一个特征提取网络，以u-net为主干网络，并在主干U-Net的下采样过程融入mobile-net，以提高算法特征提取能力，保留更多图像细节信息。其次，为解决图像像素级光照不足、网络的退化的问题，利用深度曲线估计（LE-曲线）对提取特征进行迭代，结合深度可分离卷积降低网络模型的参数量，并设计了5个非参考损失函数，提高算法在不同光照条件下的泛化能力和细节保留能力。最后，结合AD-NET（attentional denoising network）对增强后的图像进行降噪处理，以减少增强图像的噪声，使得图像更符合人眼的视觉感知。实验结果显示，本文算法在公开数据集Zero-DCE平均峰值信噪比（peak signal-to-noise ratio, PSNR）达到22.29，相比Zero-DCE ++算法提高了32%，在公开数据集LOL的PSNR达到21.15，相比SGZ算法提高了3%。可见，该算法能够有效地解决增强后图像的噪声问题，使得增强后的图像暗部和亮部区域的细节信息更加丰富，与其他主流算法相比本文算法图像质量有明显提升。

In order to address issues such as high noise, low brightness, and blurred details in low-light conditions, a new algorithm named UMDCEAD-NET, integrating zero-reference depth curves for low-light image enhancement and denoising, was developed. The algorithm's design was initially centered around a feature extraction network, employing a U-Net architecture as the backbone network. To enhance the feature extraction capabilities and preserve more detailed image information, Mobile-Net was integrated into the downsampling phase of the U-Net backbone. Subsequently, to address the issue of inadequate lighting and pixel-level image degradation, the extracted features underwent iteration using depth curve estimation （LE-curve）, in conjunction with depth separable convolution, which served to reduce the network model's parameter count. Furthermore, five non-reference loss functions were engineered to bolster the algorithm's generalization capabilities and its retention of detail under varying lighting conditions. The enhanced image was then subjected to noise reduction in tandem with AD-NET（attentional denoising network）, thereby diminishing the noise and aligning the image more closely with human visual perception. Experimental outcomes demonstrated that the proposed algorithm achieved an average PSNR （peak signal-to-noise ratio） of 22.29 on the public dataset Zero-DCE, which exceeded the performance of the Zero-DCE + + algorithm by 32%. Additionally, on the public dataset LOL, the algorithm attained an average PSNR of 21.15, outperforming the SGZ algorithm by 3%. These results indicate that the algorithm effectively mitigates noise in enhanced images, enriching the detail information in both dark and bright regions, and significantly improving image quality compared to other mainstream algorithms.