21 citations · 37 across the 18 of their papers we have counts for
6 papers · 1 filter
Trajectory Constraints for Imaging Inverse Problems
Chaoyan Huang, Haijie Yuan, Saiprasad Ravishankar
Diffusion-based and iterative methods have become effective tools for solving imaging inverse problems. Their reconstruction process naturally forms a trajectory of intermediate es…
UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights
Shijun Liang, Ismail R. Alkhouri, Siddhant Gautam +2
Recent advances in data-centric deep generative models have led to significant progress in solving inverse imaging problems. However, these models (e.g., diffusion models (DMs)) ty…
Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA
Lanlan Feng, Ce Zhu, Yipeng Liu +2
Robust tensor principal component analysis (RTPCA) aims to separate the low-rank and sparse components from multi-dimensional data, making it an essential technique in the signal p…
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at Initialization
Avrajit Ghosh, Xitong Zhang, Kenneth K. Sun +3
We introduce Optimal Eye Surgeon (OES), a framework for pruning and training deep image generator networks. Typically, untrained deep convolutional networks, which include image sa…
Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data
Anish Lahiri, Marc Klasky, Jeffrey A. Fessler +1
Reconstruction of CT images from a limited set of projections through an object is important in several applications ranging from medical imaging to industrial settings. As the num…
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising
Bihan Wen, Saiprasad Ravishankar, Yoram Bresler
Techniques exploiting the sparsity of images in a transform domain have been effective for various applications in image and video processing. Transform learning methods involve ch…