Progressive Contrast Registration for High-Fidelity Bidirectional Photoacoustic Microscopy Alignment

• URL: http://arxiv.org/abs/2602.13304v1
• Date: Mon, 09 Feb 2026 19:33:38 GMT
• Title: Progressive Contrast Registration for High-Fidelity Bidirectional Photoacoustic Microscopy Alignment
• Abstract summary: PCReg-Net is a progressive contrast-guided registration framework that performs coarse-to-fine alignment.<n>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.
• Score: 1.1597621848542221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

Related papers

• Pre-Whitening and BCJR Posterior Distillation for Bi-LSTM Detection in Faster-than-Nyquist Signaling [0.0]
  Recurrent detectors are low-complexity alternatives to the optimal Bahl-Cocke-Jelinek-Raviv (BCJR) detector for faster-than-Nyquist (FTN) signaling.<n>We show that processing nested ISI windows in separate recurrent branches improves the bit error rate (BER) of a Bi-LSTM network.
  arXiv  Detail & Related papers  (2026-09-07T17:07:49Z)
• Coupled Continuous-Discrete Generation for Scene Text Image Super-Resolution [51.31474187379532]
  Scene text image super-resolution (STISR) aims to recover visually plausible appearance while preserving character semantics from degraded inputs.<n>We present DualTSR, a unified framework that formulates STISR as coupled continuous-discrete generation.<n> Conditional flow matching restores continuous image latents, while absorbing-state discrete diffusion reconstructs text tokens.
  arXiv  Detail & Related papers  (2026-08-05T06:57:30Z)
• GB-LSR: A Fast Local Spectral Image Representation with a Single Global Bandwidth for Continuous Reconstruction and Super-Resolution [0.6138671548064355]
  We present GB-LSR, a fixed-grid local spectral representation for continuous image reconstruction.<n>We study three bandwidth-handling variants: a trainable global scalar (main), a fixed global scalar, and a per-patch bandwidth field.<n>Main variant outperforms matched-budget amortized LIIF / LTE / WIRE re-implementations by 2.8-3.6 dB PSNR and 0.11-0.15 LPIPS.<n>In a separate arbitrary-scale super-resolution (ASR) extension, GB-LSR achieves competitive PSNR-Y under a canonical-style SR protocol.
  arXiv  Detail & Related papers  (2026-06-17T21:50:05Z)
• Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders [123.58723804218151]
  Vision foundation models are bottlenecked by the quadratic cost of self-attention.<n>We present C-GSPN based on 2D spatial propagation propagation.
  arXiv  Detail & Related papers  (2026-05-30T14:29:43Z)
• GS-STVSR: Ultra-Efficient Continuous Spatio-Temporal Video Super-Resolution via 2D Gaussian Splatting [71.12303582606019]
  Continuous Video Super-Resolution (C-STVSR) aims to simultaneously enhance the spatial and frame rate of videos by arbitrary scale factors.<n>We propose GS-STVSR, an ultra-efficient C-STVSR framework based on 2D Gaussianting (2D-GS)<n>We show that GS-STVSR achieves state-of-the-art quality across all benchmarks.
  arXiv  Detail & Related papers  (2026-04-20T10:11:05Z)
• Position-Aware Scene-Appearance Disentanglement for Bidirectional Photoacoustic Microscopy Registration [6.677604052097574]
  High-speed optical-resolution photo microscopy (OR-PAM) with bidirectional scanning doubles imaging speed.<n>Existing registration methods, constrained by brightness constancy assumptions, achieve limited alignment quality.<n>We propose GPEReg-Net, a scene-appearance disentanglement framework that separates domain-inappearance scene features from domain-specific appearance codes.<n>On the OR-PAM-Reg-4K benchmark, GPEReg-Net achieves NCC of 0.953, SSIM of 0.932, and PSNR of 34.49dB, surpassing the state-of-the-art by 3.8% in SSIM and 1.99dB
  arXiv  Detail & Related papers  (2026-02-17T19:20:23Z)
• SAS-Net: Scene-Appearance Separation Network for Robust Spatiotemporal Registration in Bidirectional Photoacoustic Microscopy [1.1597621848542221]
  Optical speed-resolution photoacoustical microscopy (OR-PAM) with bidirectional scanning enables rapid functional brain imaging.<n>The proposed framework enables robust high-speed bidirectional OR-PAM for reliable quantitative and longitudinal functional imaging.
  arXiv  Detail & Related papers  (2026-02-06T21:01:27Z)
• Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection [51.56484100374058]
  We introduce a modular pipeline that improves spatial and temporal robustness without altering existing change detection networks.<n>A diffusion module synthesizes intermediate morphing frames that bridge large appearance gaps, enabling RoMa to estimate stepwise correspondences.<n>Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show consistent gains in both registration accuracy and downstream change detection.
  arXiv  Detail & Related papers  (2025-11-11T08:40:28Z)
• Distilling Parallel Gradients for Fast ODE Solvers of Diffusion Models [53.087070073434845]
  Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature.<n>Existing solver-based acceleration methods often face image quality degradation under a low-latency budget.<n>We propose the Ensemble Parallel Direction solver (dubbed as ours), a novel ODE solver that mitigates truncation errors by incorporating multiple parallel gradient evaluations in each ODE step.
  arXiv  Detail & Related papers  (2025-07-20T03:08:06Z)
• Spec2VolCAMU-Net: A Spectrogram-to-Volume Model for EEG-to-fMRI Reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net [13.510069069207548]
  High-resolution functional magnetic resonance imaging (fMRI) is essential for mapping human brain activity.<n>Existing EEG-to-fMRI generators rely on plain Convolutional Networks (CNNs) that fail to capture cross-channel time-frequency cues.<n>We propose Spec2VolCAMU-Net, a lightweight architecture featuring a Multi-directional Time-Frequency Convolutional Attention for rich feature extraction.
  arXiv  Detail & Related papers  (2025-05-14T16:18:21Z)
• Speedy MASt3R [68.47052557089631]
  MASt3R redefines image matching as a 3D task by leveraging DUSt3R and introducing a fast reciprocal matching scheme.<n>Fast MASt3R achieves a 54% reduction in inference time (198 ms to 91 ms per image pair) without sacrificing accuracy.<n>This advancement enables real-time 3D understanding, benefiting applications like mixed reality navigation and large-scale 3D scene reconstruction.
  arXiv  Detail & Related papers  (2025-03-13T03:56:22Z)
• Parallel Sequence Modeling via Generalized Spatial Propagation Network [80.66202109995726]
  Generalized Spatial Propagation Network (GSPN) is a new attention mechanism for optimized vision tasks that inherently captures 2D spatial structures.<n>GSPN overcomes limitations by directly operating on spatially coherent image data and forming dense pairwise connections through a line-scan approach.<n>GSPN achieves superior spatial fidelity and state-of-the-art performance in vision tasks, including ImageNet classification, class-guided image generation, and text-to-image generation.
  arXiv  Detail & Related papers  (2025-01-21T18:56:19Z)
• Efficient and Low-Footprint Object Classification using Spatial Contrast [0.07589017023705934]
  Event-based vision sensors traditionally compute temporal contrast that offers potential for low-power and low-latency sensing and computing. In this research, an alternative paradigm for event-based sensors using localized spatial contrast (SC) is investigated.
  arXiv  Detail & Related papers  (2023-11-06T15:24:29Z)
• Practical Conformer: Optimizing size, speed and flops of Conformer for on-Device and cloud ASR [67.63332492134332]
  We design an optimized conformer that is small enough to meet on-device restrictions and has fast inference on TPUs. Our proposed encoder can double as a strong standalone encoder in on device, and as the first part of a high-performance ASR pipeline.
  arXiv  Detail & Related papers  (2023-03-31T23:30:48Z)
• A Robust Multimodal Remote Sensing Image Registration Method and System Using Steerable Filters with First- and Second-order Gradients [7.813406811407584]
  Co-registration of multimodal remote sensing images is still an ongoing challenge because of nonlinear radiometric differences (NRD) and significant geometric distortions. In this paper, a robust matching method based on the Steerable filters is proposed consisting of two critical steps. The performance of the proposed matching method has been evaluated with many different kinds of multimodal RS images.
  arXiv  Detail & Related papers  (2022-02-27T12:22:42Z)
• Optical-Flow-Reuse-Based Bidirectional Recurrent Network for Space-Time Video Super-Resolution [52.899234731501075]
  Space-time video super-resolution (ST-VSR) simultaneously increases the spatial resolution and frame rate for a given video. Existing methods typically suffer from difficulties in how to efficiently leverage information from a large range of neighboring frames. We propose a coarse-to-fine bidirectional recurrent neural network instead of using ConvLSTM to leverage knowledge between adjacent frames.
  arXiv  Detail & Related papers  (2021-10-13T15:21:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.