Snow degradation is complex and variable， including various snowflakes， snow spots and snow streaks. To this end， we proposed a dual attention refinement desnowing network （DARDNet）. The network introduced a dimensional splitting strategy to handle two-dimensional features of channel and pixel in parallel， aiming to achieve a good trade-off between complex features and texture details. The channel attention mechanism built a module for the multiple degradation and forms a U-shaped pyramid structure to extract the depth features； the pixel attention mechanism combined the convolution to form the self-calibration module， and connected the efficient Transformer to preserve texture details； The parallel processed information streams were fused to improve the reconstruction quality of the image. Experiments were carried out on CSD， SRRS and Snow100K datasets， where PSNR reached 32.56 dB and SSIM reached 0.96 on CSD dataset. The experimental results show that our proposed method has obvious advantages in dealing with various snow degradations， which can better reconstruct the detail information and achieve satisfactory snow removal results.