Single pixel imaging at high pixel resolutions

TL;DR

This paper demonstrates high-resolution single pixel imaging at 1024x768 pixels within 0.3 seconds for sparse images, using novel sampling and reconstruction strategies that outperform traditional methods in speed and resolution.

Contribution

It introduces a new SPI framework enabling high-resolution, real-time imaging of sparse scenes with a simple, non-adaptive sampling method and efficient reconstruction algorithm.

Findings

Reconstructed images at 1024x768 resolution within 0.3 seconds.

Achieved a compression ratio of approximately 0.4%.

Supported real-time imaging at 7 Hz for sparse scenes.

Abstract

The usually reported pixel resolution of single pixel imaging (SPI) varies between $32 \times 32$ and $256 \times 256$ pixels falling far below imaging standards with classical methods. Low resolution results from the trade-off between the acceptable compression ratio, the limited DMD modulation frequency, and reasonable reconstruction time, and has not improved significantly during the decade of intensive research on SPI. In this paper we show that image measurement at the full resolution of the DMD, which lasts only a fraction of a second, is possible for sparse images or in a situation when the field of view is limited but is a priori unknown. We propose the sampling and reconstruction strategies that enable us to reconstruct sparse images at the resolution of $1024 \times 768$ within the time of $0.3~$s. Non-sparse images are reconstructed with less details. The compression ratio is on the order of $0.4 \%$ which corresponds to an acquisition frequency of $7~$Hz. Sampling is differential, binary, and non-adaptive, and includes information on multiple partitioning of the image which later allows us to determine the actual field of view. Reconstruction is based on the differential Fourier domain regularized inversion (D-FDRI). The proposed SPI framework is an alternative to both adaptive SPI, which is challenging to implement in real time, and to classical compressive sensing image recovery methods, which are very slow at high resolutions.