8 papers
CLIMB-flow: Coupled Linear Inverse posterior sampling via Multiscale-Based flow
Zeqiu Yu, Ruizhi Yuan, Mathews Jacob
Diffusion models are now widely used in Bayesian inverse problems in imaging as priors, where latent diffusion models are often used for larger scale problems to keep the computati…
Regularized joint reconstruction and slab combination for accelerated three-dimensional multi-slab diffusion-weighted imaging using multi-scale energy models
Reza Ghorbani, Jyothi Rikhab Chand, Chu-Yu Lee +2
This work presents Energy-based Profile Encoding, EPEN, a joint reconstruction framework for high-resolution diffusion-weighted MRI from undersampled 3D multi-slab k-space acquisit…
Annealed Langevin Posterior Sampling (ALPS): A Rapid Algorithm for Image Restoration with Multiscale Energy Models
Jyothi Rikhab Chand, Mathews Jacob
Solving inverse problems in imaging requires models that support efficient inference, uncertainty quantification, and principled probabilistic reasoning. Energy-Based Models (EBMs)…
Deep End-to-End Posterior ENergy (DEEPEN) for image recovery
Jyothi Rikhab Chand, Mathews Jacob
Current end-to-end (E2E) and plug-and-play (PnP) image reconstruction algorithms approximate the maximum a posteriori (MAP) estimate but cannot offer sampling from the posterior di…
MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model
Jyothi Rikhab Chand, Mathews Jacob
We propose a multi-scale deep energy model that is strongly convex in the local neighbourhood around the data manifold to represent its probability density, with application in inv…
Three-Dimensional Diffusion-Weighted Multi-Slab MRI With Slice Profile Compensation Using Deep Energy Model
Reza Ghorbani, Jyothi Rikhab Chand, Chu-Yu Lee +2
Three-dimensional (3D) multi-slab acquisition is a technique frequently employed in high-resolution diffusion-weighted MRI in order to achieve the best signal-to-noise ratio (SNR)…
Memory-efficient deep end-to-end posterior network (DEEPEN) for inverse problems
Jyothi Rikhab Chand, Mathews Jacob
End-to-End (E2E) unrolled optimization frameworks show promise for Magnetic Resonance (MR) image recovery, but suffer from high memory usage during training. In addition, these det…
Local monotone operator learning using non-monotone operators: MnM-MOL
Maneesh John, Jyothi Rikhab Chand, Mathews Jacob
The recovery of magnetic resonance (MR) images from undersampled measurements is a key problem that has seen extensive research in recent years. Unrolled approaches, which rely on…