Machine learning assisted beyond the diffraction limit imaging

点击翻译按钮获取中文摘要

We propose that a deep neural network and employment of various machine learning algorithms can significantly improve optical microscopy, enhancing its spatial resolution over a large field of view and depth of field. After its training, the only input to such a network can be an image acquired using a regular optical microscope, without any changes to its design. We will train our system using datasets generated by imaging a carefully designed and nanofabricated set of reference samples containing different materials and geometries, imaged above and below the optical diffraction limit using a combination of optical and electron microscopy. Post training, we will blindly test this approach using various samples that are imaged with low-resolution and wide-field systems. Our approach will allow us to rapidly output an image which surpasses the resolution capability of the system. This will allow us to maintain resolution while significantly surpassing the limited field of view and depth of field of conventional optical microscopy systems. Therefore, this project will culminate in the development of a universal machine learning adapter that will allow the conversion of any ordinary optical microscope into a wide field-of-view beyond the diffraction limit optical nanoscope.The proposed research program will have huge significance to any field that use microscopy tools, including, e.g., life and material sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond that, the presented approach can be enhanced by using multispectral imaging, spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better as they continue to image different samples.