DiffPR: Diffusion-Based Phase Reconstruction via Frequency-Decoupled Learning

• URL: http://arxiv.org/abs/2506.11183v1
• Date: Thu, 12 Jun 2025 17:08:45 GMT
• Title: DiffPR: Diffusion-Based Phase Reconstruction via Frequency-Decoupled Learning
• Authors: Yi Zhang,
• Abstract summary: Oversmoothing remains a persistent problem when applying deep learning to off-axis quantitative phase imaging (QPI)<n>We trace this issue to spectral bias and show that the bias is reinforced by high-level skip connections.<n>We introduce DiffPR, a two-stage frequency-decoupled framework.
• Score: 4.560284382063488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Oversmoothing remains a persistent problem when applying deep learning to off-axis quantitative phase imaging (QPI). End-to-end U-Nets favour low-frequency content and under-represent fine, diagnostic detail. We trace this issue to spectral bias and show that the bias is reinforced by high-level skip connections that feed high-frequency features directly into the decoder. Removing those deepest skips thus supervising the network only at a low resolution significantly improves generalisation and fidelity. Building on this insight, we introduce DiffPR, a two-stage frequency-decoupled framework. Stage 1: an asymmetric U-Net with cancelled high-frequency skips predicts a quarter-scale phase map from the interferogram, capturing reliable low-frequency structure while avoiding spectral bias. Stage 2: the upsampled prediction, lightly perturbed with Gaussian noise, is refined by an unconditional diffusion model that iteratively recovers the missing high-frequency residuals through reverse denoising. Experiments on four QPI datasets (B-Cell, WBC, HeLa, 3T3) show that DiffPR outperforms strong U-Net baselines, boosting PSNR by up to 1.1 dB and reducing MAE by 11 percent, while delivering markedly sharper membrane ridges and speckle patterns. The results demonstrate that cancelling high-level skips and delegating detail synthesis to a diffusion prior is an effective remedy for the spectral bias that limits conventional phase-retrieval networks.

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