Abstract: A low-light image enhancement method based on deep learning, called ADLIE (Attention-directed Low-light Image Enhancement) algorithm, is proposed for the problems of uneven illumination and lack of clear recovery details produced in the light images captured in dark scenes. First, a dual-channel attention mechanism is used to guide the attention network, which is regionally graded based on the brightness and darkness of the input image, while the dual-channel attention mechanism can also extract more better local feature information. Then, the attention map is fed into the enhancement network together with the input image. Different levels of light intensity are used for different regions to enhance the image contrast and achieve the effect of uniform exposure of the overall image. Finally, the enhancement module is added to recover the image details using the multi-layer convolutional connections in the enhancement module to obtain a more clearer high quality image. In addition, the experimental validation using open LOL dataset and LSRW dataset collected in real scenes compares classical methods such as Retinex and R2RNet on common evaluation metrics such as PSNR (peak signal-to-noise ratio), SSIM (structural similarity), CTRS (contrast ratio) and Information Entropy (information entropy). The experimental results show a significant improvement. The network restores the details of the image while improving the overall brightness of the low-light image, reduces color distortion, avoids global overexposure, and obtains a clearer and more natural image.