Aiming at the problem that deep learning-based Low-Light Image Enhancement （LLIE） techniques generally rely on paired datasets for training， considering the difficulty of acquiring paired datasets in practical applications and its possible limitation of network generalization ability， an LLIE network combining Signal-to-Noise Ratio （SNR） guided dual-branch structure and Histogram Equalization （HE） was proposed to get rid of the dependence on paired datasets. Firstly， based on the Generative Adversarial Network （GAN） framework， a dual-branch structure of Convolutional Neural Network （CNN） and Transformer was introduced， and SNR images were used to guide the network to enhance different regions of the image adaptively， thereby obtaining a balance between image enhancement and noise suppression effectively. Then， HE processed low-light images were adopted to constrain the generation results， thereby enhancing texture details in the generated images significantly. Finally， in the discriminator part， global and local discriminators were combined to ensure distributional consistency between the generated and reference images， thereby further improving visual quality of the image. To test the effectiveness of the proposed network， evaluations were conducted on LOL and LSRW test sets， and the proposed network was compared with 10 state-of-the-art methods， including both supervised and unsupervised methods. Experimental results show that on LOL dataset， the proposed network achieves both second place in Peak Signal-to-Noise Ratio （PSNR） （19.15 dB） and Structural Similarity Index （SSIM） （0.705 1）； on LSRW dataset， the proposed network achieves first and second places with PSNR of 17.28 dB and SSIM of 0.485 7， respectively. In particular， the PSNR on LSRW dataset of the proposed network is improved by 15.7% and 9.6%， respectively， compared to those of KinD （Kindling the Darkness） and EnlightenGAN （deep light Enhancement without paired supervision Generative Adversarial Network）methods. It can be seen that the excellent performance of the proposed network makes it outperforms unsupervised and some supervised methods， and the proposed network improves quality of the generated images significantly.