Improving the Quality of Unstained Transmission Electron Microscope Images Using Deep Learning

In the field of biomedical science, Transmission Electron Microscopy (TEM) is one of the essential tools for studying cellular structure and function. Traditional TEM image acquisition requires a heavy metal staining process, and unstained TEM images often suffer from issues such as low contrast, noise, and blurriness, which limit the observation of image resolution and details. We propose a workflow that utilizes deep learning to enhance the quality of unstained transmission electron microscope images, aiming to achieve high-quality images without the need for traditional staining processes. The proposed deep learning-based method provides an effective and efficient solution for enhancing the quality of unstained TEM images. This has significant practical implications for further investigations into cellular structures and biological functions, as well as improving the accuracy and reliability of TEM image analysis.