6 papers
KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems
Alireza Kheirandish, Jihoon Hong, Sara Fridovich-Keil
Diffusion models have shown promising performance as data-driven priors for computational imaging, as well as some capacity to detect out-of-distribution (OOD) images. However, exi…
Unique Determination of Variable Order in Subdiffusion from a Single Measurement
Jiho Hong, Bangti Jin, Yavar Kian
We study the inverse problem of recovering a spatially dependent variable order in a time-fractional diffusion model from the boundary flux measurement generated by a single bounda…
Unique and Stable Recovery of Space-Variable Order in Multidimensional Subdiffusion
Jiho Hong, Bangti Jin, Yavar Kian
In this work we investigate the unique identifiability and stable recovery of a spatially dependent variable-order in the subdiffusion model from the boundary flux measurement. We…
Solving Inverse Acoustic Obstacle Scattering Problem with Phaseless Far-Field Measurement Using Deep Neural Network Surrogates
Yuxin Fan, Jiho Hong, Bangti Jin
In this work, we investigate the use of deep neural networks (DNNs) as surrogates for solving the inverse acoustic scattering problem of recovering a sound-soft obstacle from phase…
Direct Algorithms for Reconstructing Small Conductivity Inclusions in Subdiffusion
Jiho Hong, Bangti Jin, Zhizhang Wu
The subdiffusion model that involves a Caputo fractional derivative in time is widely used to describe anomalously slow diffusion processes. In this work we aim at recovering the l…
Identification of a Spatially-Dependent Variable Order in One-Dimensional Subdiffusion
Jiho Hong, Bangti Jin, Yavar Kian
In this work we investigate an inverse problem of identifying a spatially variable order in the one-dimensional subdiffusion model from the boundary flux measurement. The model inv…