Low illumination image enhancement technology is one of the research hotspots of computer vision. The theoretical algorithm of Retinex assumes that the image is the product of the reflection component and the illumination component. It restores the image by removing or correcting the illumination component and combining the reflection component of the object, which is widely used in traditional algorithms and deep learning enhancement models. Spectral reflectance is the fingerprint of color, and multispectral images have more information than RGB images. Colorimetric theory and Retinex theory agree that the color of an image depends on reflection data, but spectral reflectance is obtained based on instrument measurement, and the image reflection component is obtained based on image hypothesis decomposition. The literature has not studied the enhancement of low-light images from the perspective of spectral reflectance. Inspired by Retinex theory and combined with the strong nonlinear fitting ability of deep learning, a low illumination image enhancement method based on spectral reflectance is proposed. The spectral reflectance of color is used to replace the image reflection component in the RetinexNet network, and the spectral power distribution of the CIE standard light source is used to replace the image illumination component in the network. Firstly, the spectral reflectance of normal light images in the image database is reconstructed to build a multispectral image dataset of low illumination and normal light images. Then, the deep learning network model is trained to convert low-illumination images into the multispectral images. Any low illuminance image is obtained from the multispectral image through the network model, and the multispectral image is obtained from the CIEXYZ tristimulus according to the colorimetric theory and then converted to the RGB color space for display through the standard color space.The method is trained and tested on the public LOL dataset, and the results show that this method is superior to the standard methods in image noise suppression and color restoration, which proves the superiority and effectiveness of this method for low illumination image enhancement.