In order to solve the problems of high image noise, low brightness and blurred details under low-light conditions, a low-light image enhancement and denoising algorithm (UMDCEAD-NET) incorporating a zero reference depth curve is proposed. The algorithm firstly incorporates the mobile-net module into the downsampling module of u-net to achieve effective feature extraction so as to retain more image detail information. Secondly, depth curve estimation (LE-curve) is utilised to solve the problem of insufficient illumination at the pixel level of the image. Finally, the enhanced image is combined with the AD-NET algorithm for noise reduction in order to make the image more compatible with the visual perception of the human eye. The experimental results show that the proposed algorithm achieves an average peak signal-to-noise ratio (PSNR) of 22.29 in the public dataset Zero-DCE, which is 32% higher than that of the Zero-DCE++ algorithm, and an average peak signal-to-noise ratio (PSNR) of 21.15 in the public dataset LOL, which is 3% higher than that of the SGZ algorithm. It can be seen that the algorithm can effectively solve the noise problem of the enhanced image, so that the enhanced image dark and bright regions of the detailed information is richer, compared with other mainstream algorithms the algorithm image quality has been significantly improved.