利用散射光提升成像鲁棒性与焦深感知能力
AI-interpreted Optical Scattering for Robust and Focal Depth-Aware Imaging
- 用变分自编码器分析散斑模式，实现可解释的图像重建
- 散射使数据对像素丢失更鲁棒，信息分布更均匀
- 散射模式能区分焦深信息，适合复杂场景成像
光学散射通常被视为成像质量下降的障碍。本文研究了散射在图像重建中的潜在益处。通过对比无散射的MNIST数据集与三种不同散射条件下生成的散射MNIST数据集，我们发现散射能增强数据对空间像素丢失的鲁棒性，有效分散信息。采用变分自编码器（VAE）方法，实现了与先进深度学习相当的准确率，且具有可解释的隐空间。此外，实验表明散射模式可编码焦深信息。这些发现有望推动复杂环境中更高效的成像技术发展，尤其适用于存在障碍物和三维信号的情况。
原文摘要 · Abstract (English)
Optical scattering has conventionally been regarded as an impediment in imaging research due to the degradation of image quality during reconstruction. Nevertheless, this study explores two cases in which optical scattering may serve a beneficial role in image reconstruction tasks. We compared the No Scattering MNIST dataset with three Scattering MNIST datasets, each generated under distinct scattering conditions. To assess the information content of the resulting speckle patterns, we employed a Variational Autoencoder (VAE) approach which achieves accuracy comparable to state-of-the-art deep learning approaches, but has an interpretable latent space. We find that scattering can enhance data robustness against spatial pixel loss by effectively distributing information. We also demonstrate that scattering can enable distinctions of focal depth information. We anticipate that these findings will contribute to more efficient imaging techniques, particularly in the presence of obstacles and three-dimensional signals.
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