Due to unavoidable environmental and/or technical constraints, many photographs are often taken in low-light conditions, which result in underexposure and severe noise. Existing low-light enhancement and denoising methods can deal with both problems individually, but the forced cascading of such methods does not deal well with the combined degradation of light and noise, and is also time-consuming. To address this problem, we propose an efficient network–LDNet, to perform joint low-light enhancement and denoising tasks. LDNet contains an encoder for low-light enhancement, L-Encoder, and a decoder for denoising, D-Decoder. Specifically, we customize the lighten enhancement block (LEB) in L-Encoder to recover rich texture information and luminance information. In D-Decoder, we use image adaptive projection for denoising. Furthermore, since training an end-to-end network requires paired data support, we collect a large-scale real low-light image paired dataset (LN-data). Both the proposed network and dataset provide the basis for this challenging joint task. Extensive experimental results show that our approach achieves better results in both qualitative and quantitative evaluation, notably with a PSNR value of 27.69 and an SSIM value of 0.91 on the LN-data dataset, outperforming other optimal methods.