Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.19468 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910896796729344 |
|---|---|
| author | Gruber, Nadja Schwab, Johannes Haltmeier, Markus Biguri, Ander Dlaska, Clemens Hwang, Gyeongha |
| author_facet | Gruber, Nadja Schwab, Johannes Haltmeier, Markus Biguri, Ander Dlaska, Clemens Hwang, Gyeongha |
| contents | We propose Noisier2Inverse, a correction-free self-supervised deep learning approach for general inverse problems. The proposed method learns a reconstruction function without the need for ground truth samples and is applicable in cases where measurement noise is statistically correlated. This includes computed tomography, where detector imperfections or photon scattering create correlated noise patterns, as well as microscopy and seismic imaging, where physical interactions during measurement introduce dependencies in the noise structure. Similar to Noisier2Noise, a key step in our approach is the generation of noisier data from which the reconstruction network learns. However, unlike Noisier2Noise, the proposed loss function operates in measurement space and is trained to recover an extrapolated image instead of the original noisy one. This eliminates the need for an extrapolation step during inference, which would otherwise suffer from ill-posedness. We numerically demonstrate that our method clearly outperforms previous self-supervised approaches that account for correlated noise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19468 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Noisier2Inverse: Self-Supervised Learning for Image Reconstruction with Correlated Noise Gruber, Nadja Schwab, Johannes Haltmeier, Markus Biguri, Ander Dlaska, Clemens Hwang, Gyeongha Computer Vision and Pattern Recognition Image and Video Processing Optimization and Control 94A08, 92C55 I.2.10; I.4.5; I.4.4; G.3 We propose Noisier2Inverse, a correction-free self-supervised deep learning approach for general inverse problems. The proposed method learns a reconstruction function without the need for ground truth samples and is applicable in cases where measurement noise is statistically correlated. This includes computed tomography, where detector imperfections or photon scattering create correlated noise patterns, as well as microscopy and seismic imaging, where physical interactions during measurement introduce dependencies in the noise structure. Similar to Noisier2Noise, a key step in our approach is the generation of noisier data from which the reconstruction network learns. However, unlike Noisier2Noise, the proposed loss function operates in measurement space and is trained to recover an extrapolated image instead of the original noisy one. This eliminates the need for an extrapolation step during inference, which would otherwise suffer from ill-posedness. We numerically demonstrate that our method clearly outperforms previous self-supervised approaches that account for correlated noise. |
| title | Noisier2Inverse: Self-Supervised Learning for Image Reconstruction with Correlated Noise |
| topic | Computer Vision and Pattern Recognition Image and Video Processing Optimization and Control 94A08, 92C55 I.2.10; I.4.5; I.4.4; G.3 |
| url | https://arxiv.org/abs/2503.19468 |