Due to the low-quality problems such as low brightness, poor contrast, noise, and color imbalance, the performance of the images collected in low-illumination environment is seriously affected in the process of image processing applications. The purpose of this paper is to improve the quality of low-illumination images to obtain natural and clear images with complete structure and details. Combining Retinex theory and convolutional neural network, this paper proposes a low-light image enhancement method based on MDARNet, which includes Attention mechanism module and dense convolution module to improve performance. Firstly, MDARNet uses three different scale convolution kernels that contain both two-dimensional kernels and one-dimensional kernels to perform preliminary feature extraction on the image, and the pixel attention module to perform targeted learning on multi-scale feature maps. Secondly, the skip connection structure is designed for feature extraction, so that the features of the image can achieve maximum utilization. Finally, the channel attention module and the pixel attention module are employed to perform weight learning and illumination estimation on the extracted feature maps simultaneously. The experimental results show that MDARNet can effectively improve the brightness, contrast, and color of low-light images. Compared with some classical algorithms, the MDARNet adopted in this thesis can achieve better results in visual effects and objective evaluation (PSNR, SSIM, MS-SSIM, MSE).