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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…
3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems
Narges Moeini, Namhoon Kim, Justin Romberg +1
Volume denoising is a foundational problem in computational imaging, as many 3D imaging inverse problems face high levels of measurement noise. Inspired by the strong 2D image deno…
Towards Distribution-Shift Uncertainty Estimation for Inverse Problems with Generative Priors
Namhoon Kim, Sara Fridovich-Keil
Generative models have shown strong potential as data-driven priors for solving inverse problems such as reconstructing medical images from undersampled measurements. While these p…
Geometric Algebra Planes: Convex Implicit Neural Volumes
Irmak Sivgin, Sara Fridovich-Keil, Gordon Wetzstein +1
Volume parameterizations abound in recent literature, from the classic voxel grid to the implicit neural representation and everything in between. While implicit representations ha…
ThermalNeRF: Thermal Radiance Fields
Yvette Y. Lin, Xin-Yi Pan, Sara Fridovich-Keil +1
Thermal imaging has a variety of applications, from agricultural monitoring to building inspection to imaging under poor visibility, such as in low light, fog, and rain. However, r…
Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction
Sara Fridovich-Keil, Fabrizio Valdivia, Gordon Wetzstein +2
In computed tomography (CT), the forward model consists of a linear Radon transform followed by an exponential nonlinearity based on the attenuation of light according to the Beer-…