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New methods driving the future of image capture and analysis

Deep learning to design imaging hardware

This page is dedicated to deep learning algorithms that design new types of hardware. The primary focus of the work presented here is to showcase new and improved imaging systems (cameras, microscopes, CT, MRI), which are specifically optimized to collect data by and for deep learning tasks. Please find an introduction to this area of research here, and several example projects demonstrating this new effort below.

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Duke Computational Optics Lab

This website includes research from the Computational Optics Lab at Duke University. We develop new microscopes, cameras and computer algorithms to capture better biomedical images. The lab is directed by Dr. Roarke Horstmeyer , who is a new Assistant Professor in the Biomedical Engineering Department at Duke.

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New Course: Deep Learning and Imaging

The Duke University engineering course, Machine Learning and Imaging, is now fully online. This class is an overview of machine learning and imaging science, with a focus on the intersection of the two fields. It offers much of the introductory material needed to understand the basics of machine learning for hardware design. Please find lectures, homeworks, example code and course projects here

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This page is an educational and research resource of the Computational Optics Lab at Duke University, with the goal of providing an open platform to share research at the intersection of deep learning and imaging system design.

Lab Address

Computational Optics Lab
Duke University
Fitzpatrick Center (CIEMAS) 2569
Durham, NC 27708