Single-pixel imaging 12 years on: a review

Abstract

Modern cameras typically use an array of millions of detector pixels to capture images. By contrast, single-pixel cameras use a sequence of mask patterns to filter the scene along with the corresponding measurements of the transmitted intensity which is recorded using a single-pixel detector. This review considers the development of single-pixel cameras from the seminal work of Duarte et al. up to the present state of the art. We cover the variety of hardware configurations, design of mask patterns and the associated reconstruction algorithms, many of which relate to the field of compressed sensing and, more recently, machine learning. Overall, single-pixel cameras lend themselves to imaging at non-visible wavelengths and with precise timing or depth resolution. We discuss the suitability of single-pixel cameras for different application areas, including infrared imaging and 3D situation awareness for autonomous vehicles.

Spatial Terahertz-Light Modulators for Single-Pixel Cameras

“…The aforementioned imaging approaches are the standard imaging techniques, relying on a detector array or raster scanning, however there is another alternative. Namely, using a spatially modulated light beam and a single-pixel detector to obtain an image [30]. Approaches based on this technique are commonly called computational imaging because a computer is needed to recover the image although singlepixel imaging is another common name.…”

“…The measurements are obtained sequentially as opposed to in parallel, like in detector arrays, hence this technology is usually slower. This can however be offset by the possibility of obtaining an N-pixel image using fewer measurements by employing techniques known as compressed sensing [30,31]. The main advantage to this technology is that the use of a singlepixel detector greatly enhances the robustness of the system as well as reducing the complexity and cost.…”

Speeding Up Ultrafast Spectroscopy

“…As a mathematical algorithm for fitting and reconstructing experimental data, CS falls into this latter category. Developed in the 1990s and 2000s [2][3][4], the approach is often associated with the fields of nuclear magnetic resonance (NMR) and spatial imaging, where it was first deployed [4][5][6][7]. But the concept can be applied to virtually any experimental signal.…”

“…Obtaining such measurements using a conventional signal-processing method would usually involve scanning over the full parameter space in every variable. By downsampling this parameter space for each variable, CS allows these measurements to be made with significant data-acquisition-rate benefits [1,[4][5][6][7][8]. This efficiency improvement has the added advantage that samples are less prone to degradation from long exposure to the probe.…”

“…After the success of CS in applications such as NMR [5], single-pixel imaging [4], and quantum chemistry [8], we expect this new demonstration to raise the technique's profile within the broad ultrafast-spectroscopy community. Longer term, machine learning and CS could be combined synergistically [7] to automate the process of deducing the ideal transformation that delivers the necessary sparse bases for the CS algorithm.…”