Salient object detection is developing rapidly. However, several critical challenges remain. Most existing methods struggle with high-resolution images due to either excessive computational demands or suboptimal detection quality. In addition, traditional convolutional operations commonly used in current algorithms lack targeted enhancement, resulting in inadequate edge detail extraction and blurred object boundaries. To address these limitations, this study proposes a salient object detection method based on an edge-enhanced wide decoder, which improves edge segmentation accuracy and enhances small-scale object detection while reducing computational overhead. A hybrid feature encoder combining a residual network and a Swin Transformer is employed to lower computational overhead. Traditional convolutions are replaced with a differential convolution module, where multiple types of differential convolutions are executed in parallel to extract richer edge information. A multi-scale attention module is incorporated to compute attention across four hierarchical feature layers, enabling better focus on objects of varying sizes. In addition, a multilevel wide decoder with large convolutional kernels is utilized to conduct long-range contextual modeling of fused features, effectively reducing redundant information and further boosting detection performance. Code will be released at https://github.com/wapitier/EEWDNet.