无需滤镜，用单次成像重建高光谱图像。
Filterless Snapshot Hyperspectral Imaging using Guided Patch Diffusion
- 基于小块区域的条件去噪扩散模型，实现高效重建。
- 最小补丁大小可等于点扩散函数，局部光学信息主导光谱恢复。
- 多采样生成不确定性估计，与重建误差高度相关。
本文研究从仅使用一个衍射透镜和无滤镜全色传感器捕获的 $H\times W$ 灰度快照中重建 $H\times W\times 31$ 的高光谱图像。该问题严重不适定，但我们提出的方法在模拟和实验中均实现了高质量重建。通过构建一种在小块区域上以平移不变方式运行的条件去噪扩散模型，有效利用有限训练数据。推理时，通过系统光学点扩散函数的物理一致性对各块预测进行同步。实验表明，补丁尺寸可缩小至点扩散函数级别，局部光学线索是完整光谱信息的主要来源。此外，通过多次采样，模型能提供像素级不确定性估计，其与重建误差显著相关。
原文摘要 · Abstract (English)
We consider the problem of reconstructing a HxWx31 hyperspectral image from a $H\times W$ grayscale snapshot measurement that is captured using only a single diffractive lens and a filterless panchromatic photosensor. This problem is severely ill-posed, but we present a model that produces high-quality results in simulation and experiment. We make efficient use of limited training data by creating a conditional denoising diffusion model that operates on small patches in a shift-invariant manner. During inference, we synchronize per-patch hyperspectral predictions using guidance by physical consistency with the system's optical point spread function. Our experiments reveal that the patch size can be as small as the point spread function, with local optical cues being the main source of information about complete spectra. Also, by drawing multiple samples, our model provides per-pixel uncertainty estimates that strongly correlate with reconstruction error.
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