Retinal vessel segmentation is a common task in medical image segmentation. Retinal vessel images have the characteristic of small and multiple segmentation targets. In the past, networks could effectively extract coarse blood vessels in segmentation. However, it is easy to overlook small blood vessels, the extraction of which affects the performance of the network to some extent, and even the diagnostic results. Therefore, to extract more continuous fine blood vessels while ensuring the accurate extraction of coarse blood vessels, this study uses a symmetric encoder-decoder network as the basic network and a new convolution module, DR-Conv, to prevent overfitting while improving the learning capability of the network. In the process, regarding the information loss caused by the max-pooling layer, the study uses discrete wavelet transform for image decomposition and inverse discrete wavelet transform for image reconstruction and utilizes mixed loss functions to combine the characteristics of different loss functions to compensate for the insufficient optimization ability of a single loss function. It checks the performance of the network on three public retinal vessel datasets and compares it with the latest networks, showing better performance of the proposed network.