XPAR-Net: cross-paradigm alignment and reconstruction network for high-fidelity lens-free on-chip microscopy
Scientists introduce XPAR-Net, a deep learning network for high-fidelity lens-free on-chip microscopy that improves phase and fringe reconstruction. The model outperforms existing frameworks while requiring significantly fewer computational parameters.
Why it matters
Advances in compact microscopy imaging technologies enable more efficient and accessible diagnostic tools for research and medical applications.
Scientific Reports ( 2026 ) Cite this article
We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.
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