Video denoising is a low-level task that uses the information of neighbor frames to denoise polluted frames. Using the information provided by neighboring frames, noise can be effectively removed and the denoising effect is improved. Through continuous development, single-stage and multi-stage denoising methods have been proposed. However, these existing denoising methods simply use the information provided by neighbor frames to denoise the polluted frames, paying insufficient attention to the polluted frames and making insufficient use of the information of the polluted frames. We propose a two-stage video denoising network called multiple forward-backward strategies for two-stage video denoising, which improves the denoising performance and retains more detailed information by enhancing the utilization of information of polluted frames. Extensive experiments have been conducted, and the experimental results show that the proposed method outperforms the state-of-the-art denoising methods in both qualitative and quantitative comparisons.