Graph Contrastive Learning (GCL) learns invariant representation by maximizing the consistency between different augmented graphs that share the same semantics. However, the performance of existing GCL methods is inseparable from varied manually designed augmentation techniques that randomly perturb edges/nodes/features, which unexpectedly change the semantic similarity and lead to biased structure and feature augmentation. In this paper, we propose a co-augmentation strategy for STRucture and FEature (STRFE) to eliminate augmentation bias. Specifically, we construct a relatively unbiased augmented graph by amplifying or suppressing graph frequency in the spectral domain, which promises structural consistency and feature diversity between the augmented and original graph. Moreover, we investigate external and internal contrastive loss to balance the consistency and diversity between the original and augmented graph, which facilitates preserving semantic similarity and encourages relatively unbiased structure and feature augmentation to enhance the performance of GCL. Theoretical analysis proves why our proposed structure and feature co-augmentation strategy can perform well. Extensive experiments show that STRFE achieves competitive results in three real-world datasets on different downstream tasks compared with more than ten benchmarks.