In order to improve the low light image enhancement algorithm based on convolutional neural network (CycleGAN, Retinex-Net, etc. ), which has the problems of excessive model parameters, high memory consumption and poor image recovery quality, we propose the low light image enhancement algorithm HBTNet incorporating the half-wave attention module based on the lightweight algorithm IAT. In order to improve the spatial information loss caused by frequent convolution of the network, the half-wave attention module is introduced into the network, which can effectively obtain the characteristics of wavelet domain, enrich the contextual information and improve the feature extraction ability. The quality of image recovery is improved by introducing MS-SSIM loss function used to preserve the edge and detail information of images. The experimental results show that HBTNet improves PSNR by 2. 69% and SSIM by 5. 56% compared with IAT algorithm on LOL dataset. the number of model parameters of HBTNet algorithm is only 0. 11 M, which can meet the real-time requirements of end users.