Parametric point spread function estimation for thermal imaging systems using easy-to-manufacture random pattern targets
| Type of publication: | Conference paper |
| Citation: | Piras_TARGETANDBACKGROUNDSIGNATURESX_2024 |
| Publication status: | Published |
| Booktitle: | Target and Background Signatures X: Traditional Methods and Artificial Intelligence |
| Volume: | 13199 |
| Year: | 2024 |
| Month: | November |
| Pages: | 1319905-(1-9) |
| Publisher: | SPIE |
| URL: | https://doi.org/10.1117/12.303... |
| DOI: | 10.1117/12.3031519 |
| Abstract: | Thermal and visible cameras can be characterized by their Point Spread Function (PSF), which captures the aberrations induced by the image formation process, which includes effects due to diffraction or motion. Various techniques for estimating the PSF based on a simple image of a target object that consists of a random pattern were shown to be effective. Here, we describe a computational pipeline for estimating parametric Gaussian PSFs characterized by their width, height, and orientation, based on binary random pattern targets that are suitable for thermal imaging and easy to manufacture. Specifically, we consider the influence of deviating from a strict random pattern so the targets can be manufactured with common cutting or 3D printing devices. We evaluate the estimation accuracy based on simulated patterns with varying dot, pitch, and target sizes for different values of the point spread function parameters. Finally, we show experimental examples of acquired on manufactured devices. Our results indicate that the proposed random pattern targets offer a simple and affordable approach to estimating local PSFs. |
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| Added by: | [UNK] |
| Total mark: | 0 |
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