Aiming at the problems of low contrast, weak edges and noise interference of low brightness images,a new image enhancement method based on improved quantum harmony search (QHS) algorithm to optimize fuzzy set transform in nonsubsampled Contourlet transform (NSCT) domain is proposed. First, the low brightness image is subjected to NSCT decomposition to obtain low frequency image and multi-scale high frequency sub-band images. Then, the quantum revolving door updating strategy of QHS algorithm is improved, and the improved QHS algorithm is used to optimize the transformation parameters of the fuzzy sets to realize the adaptive enhancement of low frequency images. Moreover, the Bayesian shrinkage threshold is improved to remove the noise coefficient of the high frequency sub-bands according to the energy distribution, and the edge and texture details are enhanced by the nonlinear gain function. Finally, the enhanced images of various scales are reconstructed by NSCT. Experiments are carried out on low luminance images, medical computed tomography (CT) images and infrared night vision images. The results show that, compared with the existing image enhancement methods, the proposed method not only improves the overall brightness of the image, but also has higher information entropy, contrast and clarity. In addition, the proposed method not only suppresses noise effectively, but also retains more texture details, and is suitable for low brightness image enhancement in different environments.