Change detection (CD) in remote sensing (RS) imagery plays a crucial role in Earth observation tasks using satellite data. However, due to the high similarity between the foreground and background in RS images and the insufficient utilization of difference information, current deep learning (DL)based CD methods still suffer from boundary ambiguity and false changes. To address these issues, we propose an Edge-Guided Difference Enhancement Network (EGDENet). First, multi-scale feature maps are extracted from a feature extractor. To explicitly delineate the boundaries of changed objects, we introduce the Edge Enhancement Module (EEM), which captures high-frequency edge information from bi-temporal feature maps at each scale. Then, a Dual-Branch Difference Enhancement Module (DBDEM) is designed to learn the difference between the foreground and background, guided by spatial and channel information, reducing the impact of irrelevant changes on the detection results. Additionally, a Multi-Scale Difference Feature Fusion Module (MSDFFM) is employed to enhance the robustness to multi-scale features, improving the internal completeness of the detected change regions. The experimental results demonstrate that our lightweight network effectively utilizes difference information and produces more accurate change region boundaries, outperforming state-of-the-art (SOTA) methods on four public datasets.