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[2602.09050] SAS-Net: Scene-Appearance Separation Network for Robust Spatiotemporal Registration in Bidirectional Photoacoustic Microscopy
Summary
The paper introduces SAS-Net, a novel framework for robust spatiotemporal registration in bidirectional photoacoustic microscopy, addressing challenges of domain shift and geometric distortion.
Why It Matters
This research is significant as it enhances the accuracy of brain imaging techniques, crucial for neuroscience and medical diagnostics. By improving registration methods, it enables more reliable quantitative imaging, which can lead to better understanding and treatment of neurological conditions.
Key Takeaways
- SAS-Net effectively separates domain-invariant scene content from domain-specific appearance for improved imaging.
- The framework achieves high performance metrics, including an NCC of 0.961 and SSIM of 0.894, outperforming conventional methods.
- Real-time processing capabilities are demonstrated with an inference time of 11.2 ms per frame.
- Critical losses such as domain alignment loss significantly impact performance, highlighting the importance of the proposed architecture.
- The method is poised to advance functional brain imaging, enabling longitudinal studies.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2602.09050 (eess) [Submitted on 6 Feb 2026 (v1), last revised 24 Feb 2026 (this version, v2)] Title:SAS-Net: Scene-Appearance Separation Network for Robust Spatiotemporal Registration in Bidirectional Photoacoustic Microscopy Authors:Jiahao Qin View a PDF of the paper titled SAS-Net: Scene-Appearance Separation Network for Robust Spatiotemporal Registration in Bidirectional Photoacoustic Microscopy, by Jiahao Qin View PDF HTML (experimental) Abstract:High-speed optical-resolution photoacoustic microscopy (OR-PAM) with bidirectional scanning enables rapid functional brain imaging but introduces severe spatiotemporal misalignment from coupled scan-direction-dependent domain shift and geometric distortion. Conventional registration methods rely on brightness constancy, an assumption violated under bidirectional scanning, leading to unreliable alignment. A unified scene-appearance separation framework is proposed to jointly address domain shift and spatial misalignment. The proposed architecture separates domain-invariant scene content from domain-specific appearance characteristics, enabling cross-domain reconstruction with geometric preservation. A scene consistency loss promotes geometric correspondence in the latent space, linking domain shift correction with spatial registration within a single framework. For in vivo mouse brain vasculature imaging, the proposed method achieves normalized cross-...