Progressive Contrast Registration for High-Fidelity Bidirectional Photoacoustic Microscopy Alignment

2026-02-09

AI Summary

This study addresses the challenge of domain shift and geometric misalignment in bidirectional photoacoustic microscopy, where conventional registration methods suffer from limited accuracy due to their reliance on the brightness constancy assumption. To overcome this limitation, the authors propose PCReg-Net, a progressive contrast-guided registration framework comprising four coarse-to-fine modules: a registration U-Net, a multi-scale feature extractor, a contrast module, and a feature-injected refinement U-Net. The novel contrast module explicitly identifies residual misalignments, while two reference-free temporal consistency metrics—TNCC and TNCG—are introduced for quantitative evaluation. Evaluated on the OR-PAM-Reg-4K dataset, the method achieves a normalized cross-correlation (NCC) of 0.983, SSIM of 0.982, and PSNR of 46.96 dB, surpassing existing approaches by over 14 dB and enabling real-time processing.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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-art by over 14 dB at real-time speed. Code is available at https://github.com/JiahaoQin/PCReg-Net

Problem

• photoacoustic microscopy
• bidirectional scanning
• geometric misalignment
• domain shift
• image registration

Innovation

• progressive contrast registration
• bidirectional photoacoustic microscopy
• coarse-to-fine alignment
• temporal NCC
• feature injection