ArticleBiomedical optics express2024
Improvements and validation of spatiotemporal speckle correlation model for rolling shutter speckle imaging.
Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Mapping fast tissue dynamics with long camera exposures via intensity modulation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Dynamic Light Scattering in Biomedical Applications: feature issue introduction.Biomedical optics express · 2024Article
- Rolling shutter speckle plethysmography for quantitative cardiovascular monitoring.Biomedical optics express · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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Abstract
Rolling shutter speckle imaging (RSSI) is a single-shot imaging technique that directly measures the temporal dynamics of the scattering media using a low-cost rolling shutter image sensor and vertically elongated speckles. In this paper, we derive and validate a complete spatiotemporal intensity correlation (STIC) model for RSSI, which describes the row-by-row correlation of the dynamic speckles measured with a rolling shutter in the presence of static scattering. Our new model accounts for the finite exposure time of the detector, which can be longer than the sampling interval in RSSI. We derive a comprehensive model that works for all correlation times of rolling shutter measurements. As a result, we can correctly utilize all data points in RSSI, which improves the measurement accuracy and ranges of speckle decorrelation time and dynamic scattering fraction, as demonstrated by phantom experiments. With simulations and experiments, we provide an understanding of the design parameters of RSSI and the measurement range of the speckle dynamics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.