Low-light image enhancement aims to restore the visibility of images captured under poor illumination conditions. Traditional methods often rely on complex network architectures to recover illumination; however, they tend to neglect noise removal and may even introduce additional noise during the enhancement process. Recently, some studies have proposed decoupling low-light image enhancement into two stages to improve denoising performance. Nevertheless, such two-stage networks generally lack progressive information interaction, leading to less coherent enhancement. Furthermore, these approaches frequently neglect color restoration, which is essential under low-light conditions. To address the challenges of tightly coupled brightness enhancement and denoising, severe color distortion, and insufficient inter-stage interaction in existing methods, we propose a novel Multi-Scale Progressive Low-light Enhancement Network (MSPLENet). This architecture incorporates multi-scale information and progressive enhancement strategies to gradually improve image quality through two interlinked stages, enabling a step-by-step optimization process from illumination adjustment to detail refinement. In the first stage, we design a Fast Low-light Image Enhancement Network (FLIENet) to rapidly enhance brightness and restore natural color. In the second phase, we present a Multi-branch Image Restoration Network (MIRNet), which comprises four collaborative branches functioning in unison across both spatial and frequency domains to facilitate multi-scale denoising and progressive enhancement.