Graph Neural Networks (GNNs) have recently seen extensive Collaborative Filtering (CF) applications. However, noisy interactions are usually contained in the original user-item interactions. In existing research, the influence of noisy interactions has not been simultaneously considered to be eliminated from both the embedding and sample spaces. To address this limitation, an innovative multi-view contrastive learning framework called Denoising Multi-View Graph Contrastive Learning (DMGCL) is proposed. In the sample space, denoising and augmented views are constructed based on structural and embedding similarity. In the embedding space, a complementary view is created to assist in correcting user interest modeling bias. Subsequently, contrastive learning is performed on these three views by DMGCL, denoising from both the sample space and the embedding space in a fine-grained manner. Additionally, random perturbations are introduced into the embeddings, and inter-layer contrastive learning is performed to achieve a more uniform embedding distribution. To demonstrate the performance of our proposed DMGCL, comprehensive experiments are conducted on four datasets from various domains. Evaluated on Tmall, Yelp, Gowalla, and Amazon-Book demonstrates that DMGCL outperforms the current state-of-the-art contrastive learning method with improvements of 1.58%, 1.91%, 5.29% and 5.60% on the Recall@20 metric, respectively.