Noisier2Inverse: Self-Supervised Learning for Image Reconstruction With Correlated Noise

Noisier2Inverse: Self-Supervised Learning for Image Reconstruction With Correlated Noise

We propose Noisier2Inverse, a correction-free, self-supervised deep learning method for general inverse problems. Our approach learns a reconstruction function without requiring ground truth data and is applicable in settings where measurement noise is statistically correlated. This includes applica...

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Bibliographic Details
Main Authors: Nadja Gruber, Johannes Schwab, Markus Haltmeier, Ander Biguri, Clemens Dlaska, Gyeongha Hwang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Self-supervision
image reconstruction
colored noise
incomplete data
ill-posedness
inverse problems
Online Access:https://ieeexplore.ieee.org/document/11119530/
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https://ieeexplore.ieee.org/document/11119530/

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