In the semiconductor industry, Scanning Electron Microscope (SEM) images have been commonly used for metrology and defect inspection. High-quality images are achieved by increasing frame averages, but this has a trade-off relationship with time cost. In this study, we obtained paired sets of 4-frame and 32-frame averaging semiconductor images under the same position and angle. Furthermore, we collected data from various patterns to enhance the accuracy and robustness of the experiments. We compared various IQE (Image Quality Enhancement) algorithms for the semiconductor SEM images. Several deep learning approaches were categorized and implemented with some modifications. The results were compared both quantitatively and qualitatively.