PYVALE: A Fast, Scalable, Open-Source 2D Digital Image Correlation (DIC) Engine Capable of Handling Gigapixel Images
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866908799587057664 |
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| author | Hirst, Joel Sibson, Lorna Tayeb, Adel Poole, Ben Sampson, Megan Bielajewa, Wiera Atkinson, Michael Marsh, Alex Spencer, Rory Hamill, Rob Hamelin, Cory Harte, Allan Fletcher, Lloyd |
| author_facet | Hirst, Joel Sibson, Lorna Tayeb, Adel Poole, Ben Sampson, Megan Bielajewa, Wiera Atkinson, Michael Marsh, Alex Spencer, Rory Hamill, Rob Hamelin, Cory Harte, Allan Fletcher, Lloyd |
| contents | Background: Digital Image Correlation (DIC) is a widely used full-field measurement technique, but both open-source and commercial packages often have limitations such as operating-system restrictions, lack of support for deployment on computing clusters, and poor scalability to gigapixel-scale images common in Scanning Electron Microscopy DIC (SEM-DIC). Objective: Pyvale is an open-source software package designed for sensor simulation, uncertainty quantification, placement optimization, and calibration/validation. A key component of this is the development of a dedicated 2D DIC module intended for standalone use and integration within broader workflows. Methods: Pyvale provides a user-friendly Python interface with performant compiled routines underneath. At its core is a multithreaded, reliability-guided DIC algorithm. Its open-source MIT license enables wide deployment, including on computing clusters and in automated pipelines. Results: Benchmarking with the publicly available 2D DIC challenge 2.0 dataset shows that Pyvale achieves metrological performance comparable to existing commercial and open-source DIC codes. It can correlate gigapixel-scale image pairs in under 5 minutes on high-specification desktop workstations, with memory peaking at approximately 50 GB. Conclusions: Pyvale's strong metrological foundation, coupled with its scalability for SEM-DIC, positions it as a platform for sustained, community-driven development. Its design and licensing provide a foundation for future improvements in open-source DIC and integration into experimental design and validation workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12941 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PYVALE: A Fast, Scalable, Open-Source 2D Digital Image Correlation (DIC) Engine Capable of Handling Gigapixel Images Hirst, Joel Sibson, Lorna Tayeb, Adel Poole, Ben Sampson, Megan Bielajewa, Wiera Atkinson, Michael Marsh, Alex Spencer, Rory Hamill, Rob Hamelin, Cory Harte, Allan Fletcher, Lloyd Image and Video Processing Materials Science Data Analysis, Statistics and Probability Background: Digital Image Correlation (DIC) is a widely used full-field measurement technique, but both open-source and commercial packages often have limitations such as operating-system restrictions, lack of support for deployment on computing clusters, and poor scalability to gigapixel-scale images common in Scanning Electron Microscopy DIC (SEM-DIC). Objective: Pyvale is an open-source software package designed for sensor simulation, uncertainty quantification, placement optimization, and calibration/validation. A key component of this is the development of a dedicated 2D DIC module intended for standalone use and integration within broader workflows. Methods: Pyvale provides a user-friendly Python interface with performant compiled routines underneath. At its core is a multithreaded, reliability-guided DIC algorithm. Its open-source MIT license enables wide deployment, including on computing clusters and in automated pipelines. Results: Benchmarking with the publicly available 2D DIC challenge 2.0 dataset shows that Pyvale achieves metrological performance comparable to existing commercial and open-source DIC codes. It can correlate gigapixel-scale image pairs in under 5 minutes on high-specification desktop workstations, with memory peaking at approximately 50 GB. Conclusions: Pyvale's strong metrological foundation, coupled with its scalability for SEM-DIC, positions it as a platform for sustained, community-driven development. Its design and licensing provide a foundation for future improvements in open-source DIC and integration into experimental design and validation workflows. |
| title | PYVALE: A Fast, Scalable, Open-Source 2D Digital Image Correlation (DIC) Engine Capable of Handling Gigapixel Images |
| topic | Image and Video Processing Materials Science Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2601.12941 |