The efficient utilization of electromagnetic spectrum resources has become a significant concern in the domain of wireless communications, with the electromagnetic spectrum map (EMSM) playing a crucial role in visually representing spectrum usage within a specific task area and providing valuable support for the optimization of wireless networks. To address the challenges associated with generating fine-grained EMSMs under conditions of complex scenes and limited spatial point monitoring data, an improved deep residual network (DRN) model, enhanced with a spatial attention feature block (ES-AFB), has been proposed. This model draws inspiration from image super-resolution techniques and leverages the strong spatial characteristics of EMSMs to design a deep residual network capable of extracting the correlation and spectral features of EMSMs. The enhanced spatial attention feature block is utilized to mine the intrinsic implicit spatial features of coarse-grained EMSMs. Subsequently, the data size is reconfigured through the network's multilayer up-sampling module, enabling the achievement of a more effective fine-grained image restoration. This approach allows for the generation of high-quality fine-grained EMSMs using limited coarse-grained monitoring data. The effectiveness of the algorithm has been validated through simulation experiments, with the root-mean-square error of the EMSMs generated from actual data being found to be no more than 3%.