Mohammad Emad
Eindhoven University of Technology
Electronic Systems Group
EDL P16-25 P4: Deep Learning for High-Tech Systems and Materials
Research assignment: Efficient deep learning for image restoration |
| This project concerns the application of DL for high-performance electron microscopy, in close cooperation with Thermo Fisher company. Electron microscopy enables e.g. the study of biological tissue properties and cell structures, to obtain in-depth knowledge on disease properties and possible treatments. This project focuses on using deep learning for 3D image restoration tasks like super-resolution, denoising, inpainting and segmentation of electron microscopy images. Electron microscopy images are usually very big and applying a deep neural network to the whole image consumes a lot of time and energy. Efficient deep learning platforms can be highly beneficial for this use case. Lack of training data is another challenge for image restoration of electron microscopy images. It forces us to use unsupervised or semi-supervised techniques rather than supervised methods. |
| Publications |
| DualSR: Zero-Shot Dual Learning for Real-World Super-Resolution. Mohammad Emad, Maurice Peemen, Henk Corporaal. Conference proceedings of Winter Conference on Applications of Computer Vision. Open access-green. https://drive.google.com/open?id=1lPqAt8gA0_HhaClpfN8lc86PXmz5NKdS |
| Personal information |