| EPSRC Reference: |
EP/Z534481/1 |
| Title: |
Learned Quantitative Stochastic Imaging |
| Principal Investigator: |
Pereyra, Professor M |
| Other Investigators: |
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| Project Partners: |
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| Department: |
S of Mathematical and Computer Sciences |
| Organisation: |
Heriot-Watt University |
| Scheme: |
EPSRC Fellowship TFS |
| Starts: |
13 December 2024 |
Ends: |
12 December 2029 |
Value (£): |
1,506,531
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Summary on Grant Application Form |
Digital images inform decisions that have a major impact on the economy, society, and the environment. Illustrative examples include decisions in medical practice, disaster recovery, agriculture, forestry, climate action, quality control, pollution monitoring, and defence. Such images are predominantly generated by using specialised devices (e.g., medical scanners, telescopes, and radars), which leverage decades of progress on sensor technology and instrumentation engineering. In addition to state-of-the-art hardware, modern imaging devices also rely strongly on sophisticated mathematical techniques and computational algorithms in order to transform the acquired data into high-quality images and extract useful information from them. Whereas most of us utilise photographic images as visual reminders or for social reasons, the images acquired for the aforementioned purposes are often used in a quantitative manner, as measurements of high-dimensional physical quantities of interest, so-called "quantitative imaging" (e.g., a satellite image measuring a vegetation index that is used to calculate water deficit for forest fire forecasting). Quantitative images are used as evidence in decision making or as scientific evidence, and are therefore subject to high accuracy and robustness requirements. This fellowship proposal focuses on advancing the mathematical and computational foundations that will underpin future quantitative imaging technologies.
A major challenge facing quantitative imaging sciences is that the analysed data does not contain enough information to accurately determine the exact value of the images (the data is corrupted by measurement noise and has limited resolution). The last decades have witnessed remarkable progress in quantitative estimation accuracy. However, in addition to accurate solutions, decision-making and scientific research also require a precise characterisation of the uncertainty in the delivered solutions (e.g., the degree of certainty or "error bars" attached to the water stress predictions that underpin the potential for forest fires). Unfortunately, even the most sophisticated quantitative imaging methods currently available are unable to accurately quantify the uncertainty in their solutions. This critical limitation severely hinders the value of quantitative images as evidence for decision-making and science.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Further Information: |
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| Organisation Website: |
http://www.hw.ac.uk |