Low illumination image has a number of issues, such as low recognition, low brightness, low resolution, low signal-to-noise ratio and blurred details. Therefore, a low-light image enhancement method combined with generative adversarial networks (GAN) in nonsubsampled shearlet transform (NSST) domain is proposed. First, low-light image and normal light image datasets are collected, the images are processed by RGB to HSV spatial transformation, the Hue and the Saturation components are unchanged, the Value components are decomposed at multiple scales by NSST, and the decomposed low-pass subband images are used to construct training set. Second, a low-frequency subband image enhancement model based on GAN is constructed, and the low-frequency subband image training set is used to train the model. Then, the low-illumination image to be processed is decomposed by NSST, the trained model is used to enhance the low-frequency subband image, the scale correlation coefficient is used to remove noise for each high-frequency direction subband, and the edge coefficient is enhanced by the nonlinear gain function. Finally, NSST reconstruction is performed on the low-frequency and high-frequency subband images after enhanced processing, and the reconstructed images are restored to RGB space. In terms of low-light image enhancement, compared to common methods, the results obtained by the proposed method show an average improvement of 3.89% in structural similarity and an average reduction of 1.03% in mean squared error, and when the noisy images are enhanced, the peak signal to noise ratio and continuous edge pixel ratio remain above 21 dB and 88%, respectively. The experimental results show that both visual effect and objective evaluation index of image quality of the proposed method are greatly improved compared to the common methods, which can effectively improve the low-quality problem of low-light images, and lay the foundation for the subsequent image processing analysis.