221 citations · 1.5k across the 104 of their papers we have counts for
6 papers · 1 filter
Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields
Renhao Liu, Yu Sun, Jiabei Zhu +2
Intensity diffraction tomography (IDT) refers to a class of optical microscopy techniques for imaging the 3D refractive index (RI) distribution of a sample from a set of 2D intensi…
Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative Mapping
Xiaojian Xu, Satya V. V. N. Kothapalli, Jiaming Liu +4
Purpose: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and -inhomogeneity-corrected maps…
Deformation-Compensated Learning for Image Reconstruction without Ground Truth
Weijie Gan, Yu Sun, Cihat Eldeniz +3
Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets. Recent work on Noise2Noise (N2N) has sho…
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition
Jiaming Liu, M. Salman Asif, Brendt Wohlberg +1
The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image…
CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems
Yu Sun, Jiaming Liu, Mingyang Xie +2
We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements. Unlike traditional DL methods that l…
SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
Jiaming Liu, Yu Sun, Weijie Gan +3
Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems. However, the computational and memory complexity of data-consistency lay…