Images taken under low light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of downstream tasks. It is hard for a CNN-based method to learn generalized features that can recover normal images from the ones under various unknown low light conditions. We propose to incorporate the contrastive learning into an illumination correction network to learn abstract representations to distinguish various low light conditions in the representation space, with the purpose of enhancing the generalizability of the network. Considering that light conditions can change the frequency components of the images, the representations are learned and compared in both spatial and frequency domains to make full advantage of the contrastive learning. Additionally, a grayscale self-weight perception method is used to preproccess the images to reduce the complexity of the model in coping with the uneven distribution of image illumination. The proposed method is evaluated on LOL and LOL-V2 datasets, and the results show that the proposed method achieves better qualitative and quantitative results compared with other state-of-the-art methods.