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

2026-07-24

AI Summary

Conventional imaging typically treats light scattering as a nuisance, overlooking its potential utility. This work demonstrates for the first time that optical scattering can enhance image robustness against pixel loss and encode depth-of-focus information. To investigate this, we introduce the Scattering MNIST dataset, which incorporates varying scattering conditions, and combine physically grounded optical scattering models with a variational autoencoder (VAE) to analyze speckle patterns through an interpretable latent space. Experimental results show that our approach achieves reconstruction accuracy comparable to state-of-the-art deep learning models while simultaneously enabling effective depth-of-focus discrimination and improved robustness to missing pixels.

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.

Problem

• optical scattering
• image reconstruction
• data robustness
• focal depth
• speckle patterns

Innovation

• optical scattering
• depth-aware imaging
• variational autoencoder
• speckle pattern
• robust reconstruction