Abstract: To address the problems of high pilot overhead and low accuracy in channel estimation for Simultaneously Transmitting and Reflecting Intelligent Surface (STAR-RIS) assisted mixed-field communication systems, a Multi-scale Feature-Aware Fusion Super Resolution Network (MFSRN) was proposed. Channel estimation problem was modeled as a matrix reconstruction task, firstly, common features were extracted from an upsampled channel matrix. Then, an attention gate was utilized to separate user-specific channel features, finally, reconstruction of multi-user channel matrices was completed in parallel. To enhance network performance, an Enhanced Convolution Block (ECB) and a Dual-Stream Feature-Aware (DSFA) module were designed to effectively extract both local and global features of mixed-field channel. Furthermore, a Feature Pyramid Attention (FPA) module was introduced to strengthen model's capability for multi-scale feature aggregation. Experimental results demonstrate that compared with classic DRSN and U-MLP schemes, proposed scheme achieves a lower pilot overhead while Normalized Mean Square Error (NMSE) is reduced by an average of 3.62 dB and 1.28 dB respectively at different Signal-to-Noise Ratios (SNR), shows better estimation performance.