Deep Learning for Super-Resolution in a Field Emission Scanning Electron Microscope

Abstract

A field emission scanning electron microscope (FESEM) is a complex scanning electron microscope with ultra-high-resolution image scanning, instant printing, and output storage capabilities. FESEMs have been widely used in fields such as materials science, biology, and medical science. However, owing to the balance between resolution and field of view (FOV), when locating a target using an FESEM, it is difficult to view specific details in an image with a large FOV and high resolution simultaneously. This paper presents a deep neural network to realize super-resolution of an FESEM image. This technology can effectively improve the resolution of the acquired image without changing the physical structure of the FESEM, thus resolving the constraint problem between the resolution and FOV. Experimental results show that the apply of a deep neural network only requires a single image acquired by an FESEM to be the input. A higher resolution image with a large FOV and excellent noise reduction is obtained within a short period of time. To verify the effect of the model numerically, we evaluated the image quality by using the peak signal-to-noise ratio value and structural similarity index value, which can reach 26.88 dB and 0.7740, respectively. We believe that this technology will improve the quality of FESEM imaging and be of significance in various application fields.