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

TL;DR

This paper introduces a compact lensless diffuser camera capable of capturing 5D images in a single shot, using scattering and multiplexing to encode high-dimensional data into speckle patterns, enabling efficient, switchable multi-dimensional imaging.

Contribution

The work presents a novel, cost-effective lensless imaging system that reconstructs 5D data from a single speckle pattern and can switch between different dimensional modes.

Findings

Successfully reconstructs 5D images from single-shot speckle patterns.

Achieves high-dimensional encoding with only 2.5% sampling rate.

Enables flexible switching between 5D and lower-dimensional imaging modes.

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.