Clara Hung L&S Math & Physical Sciences
Parallel Dataset Acquisitions for Lensless Imaging
Lensless imagers are low-cost, compact cameras with applications in medical imaging, photography, and more. Many designs for lensless imagers have been proposed, but the optimal design is not known as it is object-dependent. A method to capture images from different systems under similar conditions is needed to fairly compare system performance. Furthermore, as lensless imaging is moving towards data-intensive research, large-scale lensless measurement datasets are necessary for neural network evaluation. Yet, of the few existing datasets in the field, none fully address these demands.
We propose a portable data acquisition pipeline capable of capturing from multiple lensless imaging systems simultaneously, paired with a ground truth lensed image. This contribution would enable a fair comparison of multiple different lensless systems, a quantitative understanding of optimal lensless imager design, and facilitate emerging work in machine learning and information theory.
Message To Sponsor
Thank you for your generous donation! This research project is a culmination of nearly 3 years in my lab and allows me the unique opportunity to take ownership in its direction. This project will not only enable me to make contributions towards the field of computational imaging and lensless imaging, it will also be an important part of my professional development as I am preparing for graduate school and beyond.