Theoretical resolution enhancement of confocal laser-scanning microscopy (CLSM) is sacrificed for the best compromise between optical sectioning and the signal-to-noise ratio (SNR). The pixel reassignment reconstruction algorithm can improve the effective spatial resolution of CLSM to its theoretical limit. However, current implementations are not versatile and are time-consuming or technically complex. Here we present a parameter-free post-processing strategy for laser-scanning microscopy based on deep learning, which enables a spatial resolution enhancement by a factor of ∼1.3, compared to conventional CLSM. To speed up the training process for experimental data, transfer learning, combined with a hybrid dataset consisting of simulated synthetic and experimental images, is employed. The overall resolution and SNR improvement, validated by quantitative evaluation metrics, allowed us to correctly infer the fine structures of real experimental images.