Characterization of point-source transient events with a rolling-shutter compressed sensing system

• URL: http://arxiv.org/abs/2408.16868v1
• Date: Thu, 29 Aug 2024 19:22:37 GMT
• Title: Characterization of point-source transient events with a rolling-shutter compressed sensing system
• Authors: Frank Qiu, Joshua Michalenko, Lilian K. Casias, Cameron J. Radosevich, Jon Slater, Eric A. Shields,
• Abstract summary: Point-source transient events (PSTEs) pose several challenges to an imaging system. Traditional imaging systems that meet these requirements are costly in terms of price, size, weight, power consumption, and data bandwidth. We develop a novel compressed sensing algorithm adapted to the rolling shutter readout of an imaging system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point-source transient events (PSTEs) - optical events that are both extremely fast and extremely small - pose several challenges to an imaging system. Due to their speed, accurately characterizing such events often requires detectors with very high frame rates. Due to their size, accurately detecting such events requires maintaining coverage over an extended field-of-view, often through the use of imaging focal plane arrays (FPA) with a global shutter readout. Traditional imaging systems that meet these requirements are costly in terms of price, size, weight, power consumption, and data bandwidth, and there is a need for cheaper solutions with adequate temporal and spatial coverage. To address these issues, we develop a novel compressed sensing algorithm adapted to the rolling shutter readout of an imaging system. This approach enables reconstruction of a PSTE signature at the sampling rate of the rolling shutter, offering a 1-2 order of magnitude temporal speedup and a proportional reduction in data bandwidth. We present empirical results demonstrating accurate recovery of PSTEs using measurements that are spatially undersampled by a factor of 25, and our simulations show that, relative to other compressed sensing algorithms, our algorithm is both faster and yields higher quality reconstructions. We also present theoretical results characterizing our algorithm and corroborating simulations. The potential impact of our work includes the development of much faster, cheaper sensor solutions for PSTE detection and characterization.

Related papers

• bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction [57.199618102578576]
  We propose bit2bit, a new method for reconstructing high-quality image stacks at original resolution from sparse binary quantatemporal image data. Inspired by recent work on Poisson denoising, we developed an algorithm that creates a dense image sequence from sparse binary photon data. We present a novel dataset containing a wide range of real SPAD high-speed videos under various challenging imaging conditions.
  arXiv  Detail & Related papers  (2024-10-30T17:30:35Z)
• Multi-Modal Neural Radiance Field for Monocular Dense SLAM with a Light-Weight ToF Sensor [58.305341034419136]
  We present the first dense SLAM system with a monocular camera and a light-weight ToF sensor. We propose a multi-modal implicit scene representation that supports rendering both the signals from the RGB camera and light-weight ToF sensor. Experiments demonstrate that our system well exploits the signals of light-weight ToF sensors and achieves competitive results.
  arXiv  Detail & Related papers  (2023-08-28T07:56:13Z)
• Optical flow estimation from event-based cameras and spiking neural networks [0.4899818550820575]
  Event-based sensors are an excellent fit for Spiking Neural Networks (SNNs) We propose a U-Net-like SNN which, after supervised training, is able to make dense optical flow estimations. Thanks to separable convolutions, we have been able to develop a light model that can nonetheless yield reasonably accurate optical flow estimates.
  arXiv  Detail & Related papers  (2023-02-13T16:17:54Z)
• Fast Event-based Optical Flow Estimation by Triplet Matching [13.298845944779108]
  Event cameras offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.) Optical flow estimation methods that work on packets of events trade off speed for accuracy. We propose a novel optical flow estimation scheme based on triplet matching.
  arXiv  Detail & Related papers  (2022-12-23T09:12:16Z)
• Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network [38.78699830610313]
  We propose a Dual Path Denoising Network (DPDNet) for SAR image change detection. We introduce the random label propagation to clean the label noise involved in preclassification. We also propose the distinctive patch convolution for feature representation learning to reduce the time consumption.
  arXiv  Detail & Related papers  (2022-03-13T01:51:51Z)
• ESL: Event-based Structured Light [62.77144631509817]
  Event cameras are bio-inspired sensors providing significant advantages over standard cameras. We propose a novel structured-light system using an event camera to tackle the problem of accurate and high-speed depth sensing.
  arXiv  Detail & Related papers  (2021-11-30T15:47:39Z)
• Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image [94.42139459221784]
  We propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, that is based on unfolding the ISTA algorithm. Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high quality imaging performance.
  arXiv  Detail & Related papers  (2021-03-01T19:19:38Z)
• Real-time Non-line-of-sight Imaging with Two-step Deep Remapping [0.0]
  Non-line-of-sight (NLOS) imaging takes the indirect light into account. Most solutions employ a transient scanning process, followed by a back-projection based algorithm to reconstruct the NLOS scenes. Here we propose a new NLOS solution to address the above defects, with innovations on both detection equipment and reconstruction algorithm.
  arXiv  Detail & Related papers  (2021-01-26T00:08:54Z)
• StrObe: Streaming Object Detection from LiDAR Packets [73.27333924964306]
  Rolling shutter LiDARs emitted as a stream of packets, each covering a sector of the 360deg coverage. Modern perception algorithms wait for the full sweep to be built before processing the data, which introduces an additional latency. In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built.
  arXiv  Detail & Related papers  (2020-11-12T14:57:44Z)
• Learning Monocular Dense Depth from Events [53.078665310545745]
  Event cameras produce brightness changes in the form of a stream of asynchronous events instead of intensity frames. Recent learning-based approaches have been applied to event-based data, such as monocular depth prediction. We propose a recurrent architecture to solve this task and show significant improvement over standard feed-forward methods.
  arXiv  Detail & Related papers  (2020-10-16T12:36:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.