Search Results for author:
Found 6 papers, 1 papers with code
Physics-Based Learned Diffuser for Single-shot 3D Imaging
A diffuser in the Fourier space of an imaging system can encode 3D fluorescence intensity information in a single-shot 2D measurement, which is then recovered by a compressed sensing algorithm.
Memory-efficient Learning for High-Dimensional MRI Reconstruction
Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI).
How to do Physics-based Learning
The goal of this tutorial is to explain step-by-step how to implement physics-based learning for the rapid prototyping of a computational imaging system.
Memory-efficient Learning for Large-scale Computational Imaging
Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by the unrolled iterations of classical model-based reconstructions (termed physics-based networks).
Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop
Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems.
Data-Driven Design for Fourier Ptychographic Microscopy
In this work, we learn LED source pattern designs that compress the many required measurements into only a few, with negligible loss in reconstruction quality or resolution.
