English

Exploiting scattering-based point spread functions for snapshot 5D and modality-switchable lensless imaging

Optics 2025-07-21 v1 Instrumentation and Detectors

Abstract

Snapshot multi-dimensional imaging offers a promising alternative to traditional low-dimensional imaging techniques by enabling the simultaneous capture of spatial, spectral, polarization, and other information in a single shot for improved imaging speed and acquisition efficiency. However, existing snapshot multi-dimensional imaging systems are often hindered by their large size, complexity, and high cost, which constrain their practical applicability. In this work, we propose a compact lensless diffuser camera for snapshot multi-dimensional imaging (Diffuser-mCam), which can reconstruct five-dimensional (5-D) images from a single-shot 2D recording of speckle-like measurement under incoherent illumination. By employing both the scattering medium and the space-division multiplexing strategy to extract high-dimensional optical features, we show that the multi-dimensional data (2D intensity distribution, spectral, polarization, time) of the desired light field can be encoded into a snapshot speckle-like pattern via a diffuser, and subsequently decoded using a compressed sensing algorithm at the sampling rate of 2.5%, eliminating the need for multi-scanning processes. We further demonstrate that our method can be flexibly switched between 5D and selectively reduced-dimensional imaging, providing an efficient way of reducing computational resource demands. Our work presents a compact, cost-effective, and versatile framework for snapshot multi-dimensional imaging and opens up new opportunities for the design of novel imaging systems for applications in areas such as medical imaging, remote sensing, and autonomous systems.

Keywords

Cite

@article{arxiv.2507.13813,
  title  = {Exploiting scattering-based point spread functions for snapshot 5D and modality-switchable lensless imaging},
  author = {Ze Zheng and Baolei Liu and Jiaqi Song and Muchen Zhu and Yao Wang and Menghan Tian and Ying Xiong and Zhaohua Yang and Xiaolan Zhong and David McGloin and Fan Wang},
  journal= {arXiv preprint arXiv:2507.13813},
  year   = {2025}
}

Comments

10 pages, 7 figures

R2 v1 2026-07-01T04:07:33.408Z