AI-interpreted Optical Scattering for Robust and Focal Depth-Aware Imaging

• URL: http://arxiv.org/abs/2607.22867v1
• Date: Fri, 24 Jul 2026 19:14:24 GMT
• Title: AI-interpreted Optical Scattering for Robust and Focal Depth-Aware Imaging
• Abstract summary: This study explores two cases in which optical scattering may serve a beneficial role in image reconstruction tasks.<n>We find that scattering can enhance data robustness against spatial pixel loss by effectively distributing information.<n>We also demonstrate that scattering can enable distinctions of focal depth information.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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