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[2602.13304] Progressive Contrast Registration for High-Fidelity Bidirectional Photoacoustic Microscopy Alignment
Summary
This article presents PCReg-Net, a novel framework for high-fidelity alignment in bidirectional photoacoustic microscopy, significantly improving image registration quality.
Why It Matters
The development of PCReg-Net addresses critical challenges in photoacoustic microscopy, enhancing imaging speed and accuracy. This advancement is significant for researchers and practitioners in biomedical imaging, potentially leading to better diagnostic tools and research methodologies.
Key Takeaways
- PCReg-Net improves alignment quality in photoacoustic microscopy.
- The framework uses a progressive contrast-guided approach for better results.
- Achieves superior metrics (NCC of 0.983, SSIM of 0.982) compared to existing methods.
- Introduces new evaluation metrics (TNCC and TNCG) for temporal consistency.
- Code availability supports further research and application in the field.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13304 (cs) [Submitted on 9 Feb 2026] Title:Progressive Contrast Registration for High-Fidelity Bidirectional Photoacoustic Microscopy Alignment Authors:Jiahao Qin View a PDF of the paper titled Progressive Contrast Registration for High-Fidelity Bidirectional Photoacoustic Microscopy Alignment, by Jiahao Qin 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 methods, constrained by brightness constancy assumptions, achieve limited alignment quality (NCC~$\leq 0.96$). We propose PCReg-Net, a progressive contrast-guided registration framework that performs coarse-to-fine alignment through four lightweight modules: (1)~a registration U-Net for coarse alignment, (2)~a reference feature extractor capturing multi-scale structural cues, (3)~a contrast module that identifies residual misalignment by comparing coarse-registered and reference features, and (4)~a refinement U-Net with feature injection for high-fidelity output. We further propose the Temporal NCC (TNCC) and Temporal NCC Gap (TNCG) for reference-free evaluation of inter-frame temporal consistency. On OR-PAM-Reg-4K (432 test samples), PCReg-Net achieves NCC of 0.983, SSIM of 0.982, and PSNR of 46.96 dB, surpassing the state-of-the-ar...