# Single pixel imaging at high pixel resolutions

arXiv: https://arxiv.org/abs/2206.02510 · HTML: https://mlsanity.com/paper/2206.02510/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [Giga-voxel multidimensional fluorescence imaging combining single-pixel detection and data fusion](https://mlsanity.com/paper/2107.12337/) (2021)
- [Adaptive foveated single-pixel imaging with dynamic super-sampling](https://mlsanity.com/paper/1607.08236/) (2016)
- [Ultra-high-speed color imaging with single-pixel detectors under low light level](https://mlsanity.com/paper/1907.09517/) (2019)
- [Real-time single-pixel video imaging with Fourier domain regularization](https://mlsanity.com/paper/1804.10008/) (2018)
- [Single-pixel imaging with sampling distributed over simplex vertices](https://mlsanity.com/paper/1811.10348/) (2018)
- [Compressive Sampling Using a Pushframe Camera](https://mlsanity.com/paper/2104.13085/) (2021)
- [Differential real-time single-pixel imaging with Fourier domain regularization -- applications to VIS-IR imaging and polarization imaging](https://mlsanity.com/paper/2205.12111/) (2022)

## Built on by (most cited first, 1 of 1)

- [Ultra-Low Complexity On-Orbit Compression for Remote Sensing Imagery via Block Modulated Imaging](https://mlsanity.com/paper/2412.18417/) (2024)

## Abstract (arXiv)

The usually reported pixel resolution of single pixel imaging (SPI) varies between 32 × 32 and 256 × 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 × 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.