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[2602.15959] Position-Aware Scene-Appearance Disentanglement for Bidirectional Photoacoustic Microscopy Registration
Summary
This paper presents GPEReg-Net, a novel framework for improving image registration in bidirectional photoacoustic microscopy by disentangling scene features and appearance codes.
Why It Matters
The study addresses challenges in photoacoustic microscopy, particularly the misalignment caused by bidirectional scanning. By enhancing registration quality, this research can significantly improve imaging techniques, which are crucial for medical diagnostics and research.
Key Takeaways
- GPEReg-Net effectively separates scene features from appearance codes for better image registration.
- The introduction of a Global Position Encoding module enhances temporal coherence in image sequences.
- The framework outperforms existing methods in registration quality metrics such as SSIM and PSNR.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.15959 (cs) [Submitted on 17 Feb 2026] Title:Position-Aware Scene-Appearance Disentanglement for Bidirectional Photoacoustic Microscopy Registration Authors:Yiwen Wang, Jiahao Qin View a PDF of the paper titled Position-Aware Scene-Appearance Disentanglement for Bidirectional Photoacoustic Microscopy Registration, by Yiwen Wang and 1 other authors View PDF HTML (experimental) Abstract:High-speed optical-resolution photoacoustic microscopy (OR-PAM) with bidirectional raster scanning doubles imaging speed but introduces coupled domain shift and geometric misalignment between forward and backward scan lines. Existing registration methods, constrained by brightness constancy assumptions, achieve limited alignment quality, while recent generative approaches address domain shift through complex architectures that lack temporal awareness across frames. We propose GPEReg-Net, a scene-appearance disentanglement framework that separates domain-invariant scene features from domain-specific appearance codes via Adaptive Instance Normalization (AdaIN), enabling direct image-to-image registration without explicit deformation field estimation. To exploit temporal structure in sequential acquisitions, we introduce a Global Position Encoding (GPE) module that combines learnable position embeddings with sinusoidal encoding and cross-frame attention, allowing the network to leverage context from neighboring frames for impro...
