用扩散模型修复金属透镜成像缺陷，无需大量数据。
Degradation-Modeled Multipath Diffusion for Tunable Metalens Photography
- 通过正/中/负提示路径控制细节、结构与伪影平衡。
- 在真实微型相机上实现高保真锐利重建，优于现有方法。
- 适合需要小体积成像系统且追求画质的科研与工业场景。
金属透镜为超紧凑计算成像提供了巨大潜力，但面临复杂光学退化与计算复原困难的问题。现有方法通常依赖精确光学标定或海量配对数据，这对实际成像系统而言非易事。此外，推理过程缺乏控制常导致不可接受的幻觉伪影。我们提出退化建模多路径扩散方法，用于可调金属透镜摄影，利用预训练模型的强大自然图像先验，而非大规模数据集。框架采用正、中性、负提示路径，平衡高频细节生成、结构保真度与金属透镜特异性退化的抑制，并引入伪数据增强。可调解码器实现保真度与感知质量间的可控权衡。此外，设计空间变化退化感知注意力（SVDA）模块，自适应建模复杂的光学与传感器诱导退化。最后，搭建毫米级MetaCamera进行真实世界验证。大量实验表明，该方法显著优于当前最先进方法，在高保真度与清晰度重建上表现优异。
原文摘要 · Abstract (English)
Metalenses offer significant potential for ultra-compact computational imaging but face challenges from complex optical degradation and computational restoration difficulties. Existing methods typically rely on precise optical calibration or massive paired datasets, which are non-trivial for real-world imaging systems. Furthermore, a lack of control over the inference process often results in undesirable hallucinated artifacts. We introduce Degradation-Modeled Multipath Diffusion for tunable metalens photography, leveraging powerful natural image priors from pretrained models instead of large datasets. Our framework uses positive, neutral, and negative-prompt paths to balance high-frequency detail generation, structural fidelity, and suppression of metalens-specific degradation, alongside \textit{pseudo} data augmentation. A tunable decoder enables controlled trade-offs between fidelity and perceptual quality. Additionally, a spatially varying degradation-aware attention (SVDA) module adaptively models complex optical and sensor-induced degradation. Finally, we design and build a millimeter-scale MetaCamera for real-world validation. Extensive results show that our approach outperforms state-of-the-art methods, achieving high-fidelity and sharp image reconstruction. More materials: https://dmdiff.github.io/.
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