基于无训练神经先验的相位图像重建

中文摘要 AI 辅助

傅里叶相位编码了重要的空间图像结构，但在没有测量频谱幅度的情况下恢复图像需要额外的约束，并使得绝对强度变得模糊。我们提出了一种基于投影的两阶段框架，该框架将傅里叶相位和空间支持约束与图像特定的神经先验相结合。第一阶段交替进行约束强制和正则化神经先验更新，而第二阶段仅执行相位/支持细化，并保证收敛。我们在相同的77张显微图像上评估了两种神经先验实现，并将它们与仅约束的基线进行了比较。经过500次最终细化迭代后，最佳变体实现了31.41 dB的合并PSNR、35.75 dB的平均PSNR和0.9531的平均SSIM，相对于基线，合并PSNR提高了1.51 dB，合并MSE降低了29.3%。结果证明了在所评估的迭代预算下，将神经引导与显式约束细化相结合的好处，同时也表明仅较低的相位残差并不能保证更高的重建精度。

英文摘要

Fourier phase encodes important spatial image structure, but recovering an image without measured spectral magnitude requires additional constraints and leaves absolute intensity ambiguous. We propose a projection-based two-stage framework that combines Fourier-phase and spatial-support constraints with an image-specific neural prior. The first stage alternates constraint enforcement with regularized neural-prior updates, while the second performs phase/support refinement alone with guaranteed convergence. We evaluate two neural-prior implementations on the same 77 microscopy images and compare them with a constraint-only baseline. After 500 final refinement passes, the best-performing variant achieves 31.41 dB pooled PSNR, 35.75 dB mean PSNR, and 0.9531 mean SSIM, improving pooled PSNR by 1.51~dB and reducing pooled MSE by 29.3% relative to the baseline. The results demonstrate the benefit of combining neural guidance with explicit constraint refinement at the evaluated iteration budget, while showing that lower phase residual alone does not guarantee greater reconstruction accuracy.

发表机构

• University of Illinois at Chicago（伊利诺伊大学芝加哥分校）

机构由 AI 辅助整理，请以论文原文为准。