221 citations · 1.5k across the 104 of their papers we have counts for
16 papers · 1 filter
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
Jiaming Liu, Rushil Anirudh, Jayaraman J. Thiagarajan +4
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage i…
DOLPH: Diffusion Models for Phase Retrieval
Shirin Shoushtari, Jiaming Liu, Ulugbek S. Kamilov
Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements. Since the problem is ill-posed, the recovery requires pr…
Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach
Paul Goyes, Edwin Vargas, Claudia Correa +4
Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Pre-processing approaches, such as missing trace or shot interpol…
Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model
Junhao Hu, Shirin Shoushtari, Zihao Zou +3
Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors. Plug-and-play priors (PnP) and…
SINCO: A Novel structural regularizer for image compression using implicit neural representations
Harry Gao, Weijie Gan, Zhixin Sun +1
Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression. An image can be compressed by training an INR model w…
Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees
Weijie Gan, Chunwei Ying, Parna Eshraghi +7
Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models-implicit neural networks with effectively infinite…