Convolutional neural networks (CNNs) with a powerful feature extraction ability have raised the performance of salient object detection (SOD) to a unique level, and how to effectively decode the rich features from CNN is the key to improving the performance of the SOD model. Some previous works ignored the differences between the high-level and low-level features and neglected the information loss during feature processing, making them fail in some challenging scenes. To solve this problem, we propose a discriminative feature extraction and details preservation network (D2Net) for SOD. According to the different characteristics of high-level and low-level features, we design a residual optimization module for filtering complex background noise in shallow features and a pyramid feature extraction module to eliminate the information loss caused by atrous convolution in high-level features. Furthermore, we design a features aggregation module to aggregate the elaborately processed high-level and low-level features, which fully considers the performance of different level features and preserves the delicate boundary of salient object. The comparisons with 17 existing state-of-the-art SOD methods on five popular datasets demonstrate the superiority of the proposed D2Net, and the effectiveness of each proposed module is verified through numerous ablation experiments.