6 papers · 1 filter
Detection of Adversarial Attacks on Super-Resolvers Using Spectral Features
Emma J. Reid, Haley Duba-Sullivan, Tony G. Allen
The integration of deep learning models into image preprocessing pipelines such as super-resolution introduces a largely unexplored attack vector for adversaries targeting downstre…
Deep Pseudo-Proximal Map: A Self-Supervised Data-Fitting Agent for Iterative Reconstruction
Haley Duba-Sullivan, Emma J. Reid, Charles A. Bouman +1
Iterative algorithms for inverse imaging problems split reconstruction into alternating subproblems, one of which requires evaluating the data-fitting proximal map. In many practic…
Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model
Haley Duba-Sullivan, Patxi Fernandez-Zelaia, Obaidullah Rahman +1
Computed tomography (CT) throughput is limited by scan time, which grows with both the number of projections acquired and the detector integration time for each projection. Reconst…
Regularizing INR with diffusion prior self-supervised 3D reconstruction of neutron computed tomography data
Maliha Hossain, Haley Duba-Sullivan, Amirkoushyar Ziabari
Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently…
Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography
Timofey Efimov, Singanallur Venkatakrishnan, Maliha Hossain +2
Diffusion models have emerged as powerful priors for solving inverse problems in computed tomography (CT). In certain applications, such as neutron CT, it can be expensive to colle…
The Double-Edged Sword of Data-Driven Super-Resolution: Adversarial Super-Resolution Models
Haley Duba-Sullivan, Steven R. Young, Emma J. Reid
Data-driven super-resolution (SR) methods are often integrated into imaging pipelines as preprocessing steps to improve downstream tasks such as classification and detection. Howev…