# PCReg-Net: Progressive Contrast-Guided Registration for Cross-Domain Image Alignment

arXiv: https://arxiv.org/abs/2602.13304 · HTML: https://mlsanity.com/paper/2602.13304/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [SynthMorph: learning contrast-invariant registration without acquired images](https://mlsanity.com/paper/2004.10282/) (2020)
- [Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://mlsanity.com/paper/2006.16148/) (2020)
- [Recursive Cascaded Networks for Unsupervised Medical Image Registration](https://mlsanity.com/paper/1907.12353/) (2019)
- [uniGradICON: A Foundation Model for Medical Image Registration](https://mlsanity.com/paper/2403.05780/) (2024)
- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration](https://mlsanity.com/paper/2402.18933/) (2024)
- [Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method](https://mlsanity.com/paper/2306.16950/) (2023)
- [Zoom and Shift are All You Need](https://mlsanity.com/paper/2406.08866/) (2024)
- [DUAL: Dynamic Uncertainty-Aware Learning](https://mlsanity.com/paper/2506.03158/) (2025)
- [Learning Domain-Invariant Representations for Cross-Domain Image Registration via Scene-Appearance Disentanglement](https://mlsanity.com/paper/2601.08875/) (2026)
- [MSMF: Multi-Scale Multi-Modal Fusion for Enhanced Stock Market Prediction](https://mlsanity.com/paper/2409.07855/) (2024)
- [GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split Attention](https://mlsanity.com/paper/2501.01960/) (2024)

## Abstract (arXiv)

Deformable image registration across heterogeneous domains remains challenging because coupled appearance variation and geometric misalignment violate the brightness constancy assumption underlying conventional methods. 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 initial coarse alignment, (2)~a reference feature extractor capturing multi-scale structural cues from the fixed image, (3)~a multi-scale contrast module that identifies residual misalignment by comparing coarse-registered and reference features, and (4)~a refinement U-Net with feature injection that produces the final high-fidelity output. We evaluate on the FIRE-Reg-256 retinal fundus benchmark, demonstrating improvements over both traditional and deep learning baselines. Additional experiments on two microscopy benchmarks further confirm cross-domain applicability. With only 2.56M parameters, PCReg-Net achieves real-time inference at 141 FPS. Code is available at https://github.com/JiahaoQin/PCReg-Net.