The quality of low-light images is poor and Low-Light Image Enhancement （LLIE） aims to improve the visual quality. Most of LLIE algorithms focus on enhancing luminance and contrast， while neglecting details. To solve this issue， a Progressive Enhancement algorithm for low-light images based on Layer Guidance （PELG） was proposed， which enhanced algorithm images to a suitable illumination level and reconstructed clear details. First， to reduce the task complexity and improve the efficiency， the image was decomposed into several frequency components by Laplace Pyramid （LP） decomposition. Secondly， since different frequency components exhibit correlation， a Transformer-based fusion model and a lightweight fusion model were respectively proposed for layer guidance. The Transformer-based model was applied between the low-frequency and the lowest high-frequency components. The lightweight model was applied between two neighbouring high-frequency components. By doing so， components were enhanced in a coarse-to-fine manner. Finally， the LP was used to reconstruct the image with uniform brightness and clear details. The experimental results show that， the proposed algorithm achieves the Peak Signal-to-Noise Ratio （PSNR） 2.3 dB higher than DSLR （Deep Stacked Laplacian Restorer） on LOL（LOw-Light dataset）-v1 and 0.55 dB higher than UNIE （Unsupervised Night Image Enhancement） on LOL-v2. Compared with other state-of-the-art LLIE algorithms， the proposed algorithm has shorter runtime and achieves significant improvement in objective and subjective quality， which is more suitable for real scenes.