Single Pixel Imaging at MHz Rates via Cyclic Hadamard Patterns

In this project we focused on speeding up single pixel imaging systems and until now managed to achieve modulation rates of 2.4 MHz. This is two orders of magnitudes above the currently available modulation rates! Also, we developed algorithms for correcting motion errors that occur when moving faster than the imaging rate, this way enabling even faster imaging.

Short Project Overview

Full details of our work are found in the following two papers:

 1. Fast cyclic coding and imaging system

Single pixel imaging at megahertz switching rates via cyclic Hadamard masks.

Hahamovich E*, Monin S*, Hazan Y, Rosenthal A. “Spatial light modulation at megahertz switch rates via cyclic Hadamard masks.” Nature Communications, 2021

 * Equal contribution


Optical imaging is commonly performed with either a camera and wide-field illumination or with a single detector and a scanning collimated beam; unfortunately, these options do not exist at all wavelengths. Single-pixel imaging offers an alternative that can be performed with a single detector and wide-field illumination, potentially enabling imaging applications in which the detection and illumination technologies are immature. However, single-pixel imaging currently suffers from low imaging rates owing to its reliance on configurable spatial light modulators, generally limited to 22 kHz rates. We develop an approach for rapid single-pixel imaging which relies on cyclic patterns coded onto a spinning mask and demonstrate it for in vivo imaging of C. elegans worms. Spatial modulation rates of up to 2.4 MHz, imaging rates of up to 72 fps, and image-reconstruction times of down to 1.5 ms are reported, enabling real-time visualization of dynamic objects.

2. Motion correction algorithms

Single-pixel imaging of dynamic objects using multi-frame motion estimation.

 Monin S, Hahamovich E, Rosenthal A. “Single-pixel imaging of dynamic objects using multi-frame motion estimation.” Scientific reports, 2021


Single-pixel imaging (SPI) enables the visualization of objects with a single detector by using a sequence of spatially modulated illumination patterns. For natural images, the number of illumination patterns may be smaller than the number of pixels when compressed-sensing algorithms are used. Nonetheless, the sequential nature of the SPI measurement requires that the object remains static until the signals from all the required patterns have been collected. In this paper, we present a new approach to SPI that enables imaging scenarios in which the imaged object, or parts thereof, moves within the imaging plane during data acquisition. Our algorithms estimate the motion direction from inter-frame cross-correlations and incorporate it in the reconstruction model. Moreover, when the illumination pattern is cyclic, the motion may be estimated directly from the raw data, further increasing the numerical efficiency of the algorithm. A demonstration of our approach is presented for both numerically simulated and measured data.