6 papers
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…
ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images
Haley Duba-Sullivan, Emma J. Reid, Sophie Voisin +2
Multispectral imaging (MSI) plays a critical role in material classification, environmental monitoring, and remote sensing. However, MSI sensors typically have wavelength-dependent…
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…
Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction
Haley Duba-Sullivan, Aniket Pramanik, Venkatakrishnan Singanallur +1
Cone-beam X-ray computed tomography (XCT) is an essential imaging technique for generating 3D reconstructions of internal structures, with applications ranging from medical to indu…
2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts
Haley Duba-Sullivan, Obaidullah Rahman, Singanallur Venkatakrishnan +1
X-ray computed tomography (XCT) is a key tool in non-destructive evaluation of additively manufactured (AM) parts, allowing for internal inspection and defect detection. Despite it…