Low-light image enhancement is a hot topic as the low-light image cannot accurately reflect the content of objects. The use of low-light image enhancement technology can effectively restore the color and texture information. Different from the traditional low-light image enhancement method that is directly from low-light to normal-light, the method of low-light image enhancement based on multispectral reconstruction is proposed. The key point of the proposed method is that the low-light image is firstly transformed to the spectral reflectance space based on a deep learning model to learn the end-to-end mapping relationship from a low-light image to a normal-light multispectral image. Then the corresponding normal-light color image is rendered from the reconstructed multispectral image and the enhancement of the low-light image is completed. The motivation behind the proposed method is whether the low-light image enhancement through multispectral reconstruction will help to improve the enhancement performance or not. The verification of the proposed method based on the commonly used LOL dataset showed it outperforms the traditional direct enhancement methods, however, the underlying mechanism of the method is still to be further studied.