Company Filing History:
Years Active: 2025
Title: Innovations by Sandra Van Aert in Electron Microscopy
Introduction
Sandra Van Aert is a prominent inventor based in Hove, Belgium. She has made significant contributions to the field of electron microscopy, particularly in reducing image artefacts through innovative methods. Her work is crucial for enhancing the quality of images obtained from electron microscopy, which is vital for various scientific and industrial applications.
Latest Patents
Sandra Van Aert holds a patent for a method aimed at reducing noise and artefacts in electron microscopy images. The patent describes a process for training an artificial neural network (ANN) to improve image quality. This involves generating a plurality of training image pairs, where each pair consists of an undistorted synthetic specimen image and a distorted image created by simulating additional noise and artefact features. The ANN is trained using these distorted images as input and the corresponding undistorted images as output. An adversarial training strategy is employed, where the ANN acts as a generator network, while a further ANN serves as a discriminator network. This innovative approach optimizes the parameters of both networks using generator and discriminator loss functions, allowing for effective adversarial training.
Career Highlights
Sandra is affiliated with the University of Antwerp, where she continues to advance her research in electron microscopy and artificial intelligence. Her work has garnered attention for its potential to significantly improve imaging techniques in various scientific fields.
Collaborations
Sandra collaborates with notable colleagues such as Ivan Pedro Lobato Hoyos and Thomas Friedrich. Their combined expertise contributes to the advancement of research in electron microscopy and related technologies.
Conclusion
Sandra Van Aert's innovative work in reducing image artefacts in electron microscopy showcases her dedication to enhancing scientific imaging techniques. Her contributions are paving the way for improved methodologies in the field, benefiting researchers and industries alike.