Single-image desnowing is an important subtask in the field of image restoration. Its primary challenges lie in snow particle occlusion and snow-fog blur, which degrade image quality and affect the performance of downstream visual tasks. To address the limitations of existing methods in feature modeling and expert selection adaptability, a single-image desnowing model named SynergyRestorer was proposed. The model is based on a complementary mixture of experts and an agreement-biased sub-network routing scheme. A complementary mixture of experts decoder was designed to capture complementary information across multi-dimensional features by combining specialized and cooperative experts, thereby enhancing the model′s representation capacity. An agreement-biased sub-network router was also introduced to fuse multi-source features and incorporate agreement signals. It dynamically balanced coordination and conflict among features, improving the discriminative and adaptive capacity of expert selection. Experimental results showed that the proposed method achieved an average PSNR of 33.71 dB and SSIM of 0.950 on three benchmark datasets: CSD, Snow100K and SRRS. The results validate its effectiveness in complex snowy scene restoration tasks.