无需真值样本，可处理相关噪声的图像重建新方法
Noisier2Inverse: Self-Supervised Learning for Image Reconstruction with Correlated Noise
- 通过生成更噪数据，在测量空间中直接学习重建函数
- 在相关噪声场景下优于已有自监督方法，无需推理时外推
- 适合断层扫描、显微成像等物理测量中的噪声建模
我们提出 Noisier2Inverse，一种无需真值样本的通用逆问题图像重建自监督深度学习方法，适用于测量噪声具有统计相关性的场景。这类情况包括计算机断层扫描中探测器缺陷或光子散射导致的相关噪声模式，以及显微成像和地震成像中物理测量过程引入的噪声依赖性。与 Noisier2Noise 类似，本方法的关键是生成更噪数据供网络学习，但其损失函数作用于测量空间，目标是从噪声数据中恢复外推后的图像，而非原始噪声图像。该设计避免了推理阶段的外推步骤，从而规避了病态问题。数值实验表明，该方法在处理相关噪声方面显著优于现有自监督方法。
原文摘要 · Abstract (English)
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.
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