Digital Objective Lens: A Lightweight Neural Network for Super-Resolution of SEM Images

Scanning electron microscopy (SEM) is a crucial and frequently employed tool for material characterization. However, owing to the hardware limitations of SEM, researchers often face a trade-off when observing materials: they must either use the fast scanning mode to acquire real-time images with poor details or wait 20 to 160 s for the slow scanning mode to obtain a high-resolution (HR) image with rich details. This significantly reduces observation efficiency and prevents process-level observations. In this study, we propose a wavelet-based lightweight super-resolution (SR) network (WLSR), which serves as a “digital objective lens”: by performing rapid SR on fast-scan SEM images, 2× or 4× digital magnification of the field of view with slow-scan image quality can be obtained while large-scale observation of the sample is conducted. A channel–spatial attention block and a wavelet-based upscaling module are introduced into the network structure, and a novel loss function combining a wavelet distillation loss and a perceptual loss is proposed for network training. WLSR was trained and evaluated on paired fast-/slow-scan SEM images of AgCl@Ag specimens, in which all LR inputs are genuine fast-scan images that are jointly denoised and super-resolved. Compared with existing lightweight SR networks, WLSR generates SEM SR images with richer details, superior subjective visual quality, and competitive objective evaluation metrics at an exceptionally fast inference speed of 3.7 ms per image on an NVIDIA GV100 GPU, indicating the feasibility of real-time deployment during live SEM observation. Through WLSR, the quality of fast-scan SEM images is significantly improved, which can enhance the efficiency and accuracy of sample observation.