<p>The advent of deep learning has significantly propelled the advancement of low-light image enhancement techniques, yielding promising experimental outcomes. However, a series of image degradation problems such as noise and texture details have not been effectively handled, leaving room for further improvement of low-light image enhancement performance. In this work, we introduce a novel framework, the gradient and semantic-aware transformer network (GSTN), specifically tailored for low-light image enhancement. Our model comprises three pivotal components: the pre-lighten network (PLNet), which serves to light up the image to present more details and extract the illumination feature; the prior-guided enhancement module, designed to restore image details and mitigate noise leveraging the original gradient features; and the illuminance adjustment module (IAM), which refines the illumination of the enhanced image. In addition, we introduce discrete wavelet transform to implement cross-domain feature interactions and multi-scale feature fusion. Extensive experiments show that that our methods obtains better results in comparison with some state-of-the-art low-light image enhancement methods on different low-light datasets.</p>