发表机构
University of Maryland – College Park; Lockheed Martin（马里兰大学帕克分校; 洛克希德·马丁公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究探索光学散射在图像重建任务中有益的情况，通过比较无散射与散射MNIST数据集，采用VAE方法评估散斑图案信息内容，发现散射可增强数据鲁棒性及区分焦深信息，有助于高效成像技术发展。
AI 中文摘要
传统上，光学散射被视为成像研究中的一个障碍，因为在重建过程中图像质量会下降。然而，本研究探索了光学散射在图像重建任务中可能发挥有益作用的两种情况。我们将无散射MNIST数据集与三个在不同散射条件下生成的散射MNIST数据集进行了比较。为了评估所得散斑图案的信息内容，我们采用了变分自编码器（VAE）方法，该方法实现了与当前最先进的深度学习方法相当的准确性，并且具有可解释的潜在空间。我们发现散射可以通过有效分布信息来增强数据对空间像素损失的鲁棒性。我们还证明散射可以区分焦深信息。我们预计这些发现将有助于开发更高效的成像技术，特别是在存在障碍物和三维信号的情况下。
英文摘要
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.
