The non-linearity between human perception and image brightness levels results in different definitions of NORMAL-light. Thus, most existing low-light image enhancement methods which produce one-to-one mapping can not meet the aesthetic demand. Other pioneers enhance low-light images guided by a given value. However, the inherent problem of non-linearity will cause poor usability. To this end, we propose a user-friendly neural network for multi-level low-light image enhancement. Inspired by style transfer, our method decomposes an image into content component feature and luminance component feature in the latent space. Then we enhance the image brightness to different levels by concatenating the content components from low-light images and the luminance components from reference images. The network meets various user requirements by selecting different brightness references. Moreover, information except for brightness is preserved to alleviate color distortion. Extensive experiments demonstrate the superiority of our network against existing methods.