Summary
When viewed under coherent imaging conditions (e.g. laser illumination), scattering materials such as biological tissues create noise-like images known as speckle. Despite their seemingly random nature, speckle patterns have strong statistical correlation properties that are highly informative of the material producing them. These can be used to enable remarkable imaging capabilities, not possible with current state of the art, for example seeing through highly scattering layers.
Unfortunately, realizing these in practical settings (tissue imaging, fluorescence microscopy) remains a challenge. Research efforts are hindered by a lack of modeling tools, resulting in an incomplete understanding of speckle properties.
This project aims to use computational techniques from computer vision and computer graphics to greatly enhance our understanding of speckle statistics, and significantly expand the scope of their applications.
To this end, the project will explore algorithmic tools newly-developed by the PI that can accurately and efficiently simulate speckle patterns, to formulate better models of speckle formation.
We will exploit our new understanding to develop new types of computational imaging systems that can directly measure speckle correlation, rather than the traditional pipeline where one captures speckle images and estimates their correlations algorithmically in post-processing.
Finally, we will exploit these tools in multiple computational imaging applications, including: (i) Acquiring material parameters: estimating the type, size and density of particles composing a material of interest. (ii) Imaging fluorescent sources deep inside scattering tissue. (iii) Adaptive optics imaging.
Potential impact is anticipated in numerous areas
where speckle-based imaging techniques hold promise, including medicine (increased depth penetration of tissue imaging techniques) and material fabrication and analysis (accurate characterization of scattering materials).
Unfortunately, realizing these in practical settings (tissue imaging, fluorescence microscopy) remains a challenge. Research efforts are hindered by a lack of modeling tools, resulting in an incomplete understanding of speckle properties.
This project aims to use computational techniques from computer vision and computer graphics to greatly enhance our understanding of speckle statistics, and significantly expand the scope of their applications.
To this end, the project will explore algorithmic tools newly-developed by the PI that can accurately and efficiently simulate speckle patterns, to formulate better models of speckle formation.
We will exploit our new understanding to develop new types of computational imaging systems that can directly measure speckle correlation, rather than the traditional pipeline where one captures speckle images and estimates their correlations algorithmically in post-processing.
Finally, we will exploit these tools in multiple computational imaging applications, including: (i) Acquiring material parameters: estimating the type, size and density of particles composing a material of interest. (ii) Imaging fluorescent sources deep inside scattering tissue. (iii) Adaptive optics imaging.
Potential impact is anticipated in numerous areas
where speckle-based imaging techniques hold promise, including medicine (increased depth penetration of tissue imaging techniques) and material fabrication and analysis (accurate characterization of scattering materials).
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101043471 |
| Start date: | 01-01-2023 |
| End date: | 31-12-2027 |
| Total budget - Public funding: | 2 125 000,00 Euro - 2 125 000,00 Euro |
Cordis data
Original description
When viewed under coherent imaging conditions (e.g. laser illumination), scattering materials such as biological tissues create noise-like images known as speckle. Despite their seemingly random nature, speckle patterns have strong statistical correlation properties that are highly informative of the material producing them. These can be used to enable remarkable imaging capabilities, not possible with current state of the art, for example seeing through highly scattering layers.Unfortunately, realizing these in practical settings (tissue imaging, fluorescence microscopy) remains a challenge. Research efforts are hindered by a lack of modeling tools, resulting in an incomplete understanding of speckle properties.
This project aims to use computational techniques from computer vision and computer graphics to greatly enhance our understanding of speckle statistics, and significantly expand the scope of their applications.
To this end, the project will explore algorithmic tools newly-developed by the PI that can accurately and efficiently simulate speckle patterns, to formulate better models of speckle formation.
We will exploit our new understanding to develop new types of computational imaging systems that can directly measure speckle correlation, rather than the traditional pipeline where one captures speckle images and estimates their correlations algorithmically in post-processing.
Finally, we will exploit these tools in multiple computational imaging applications, including: (i) Acquiring material parameters: estimating the type, size and density of particles composing a material of interest. (ii) Imaging fluorescent sources deep inside scattering tissue. (iii) Adaptive optics imaging.
Potential impact is anticipated in numerous areas
where speckle-based imaging techniques hold promise, including medicine (increased depth penetration of tissue imaging techniques) and material fabrication and analysis (accurate characterization of scattering materials).
Status
SIGNEDCall topic
ERC-2021-COGUpdate Date
09-02-2023
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