Generative AI for Solving Inverse Problems in Computational Imaging

Generative AI offers great promise for effectively solving challenging ill-posed inverse problems, transforming the way we measure and infer the physical world around us, and enabling exciting new user-centric capabilities.

Authors

Institutions

Publication Details

Journal
XRDS Crossroads The ACM Magazine for Students
Published
2024-12-01
DOI
https://doi.org/10.1145/3703401
Citations
1
Primary Topic
Image and Signal Denoising Methods
Type
article
Field-Weighted Citation Impact
0.25
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generative AI for Solving Inverse Problems in Computational Imaging

Elias Nehme, Tomer Michaeli
1 citations
XRDS Crossroads The ACM Magazine for Students
Image and Signal Denoising Methods
0.25
article

Generative AI for Solving Inverse Problems in Computational Imaging

Elias Nehme, Tomer Michaeli
article en
1 citations

Abstract

Generative AI offers great promise for effectively solving challenging ill-posed inverse problems, transforming the way we measure and infer the physical world around us, and enabling exciting new user-centric capabilities.

XRDS Crossroads The ACM Magazine for StudentsVol. 31(2)
Technion – Israel Institute of Technology (IL)
Openalex Percentile: Top 43%
Image and Signal Denoising Methods
0.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Generative AI for Solving Inverse Problems in Computational Imaging — Elias Nehme, Tomer Michaeli · XRDS Crossroads The ACM Magazine for Students (2024) | TGRS Research Map | TGRS