Fingerprint dense registration aims to finely align fingerprint pairs at the
pixel level, thereby reducing intra-class differences caused by distortion.
Unfortunately, traditional methods exhibited subpar performance when dealing
with low-quality fingerprints while suffering from slow inference speed.
Although deep learning based approaches shows significant improvement in these
aspects, their registration accuracy is still unsatisfactory. In this paper, we
propose a Phase-aggregated Dual-branch Registration Network (PDRNet) to
aggregate the advantages of both types of methods. A dual-branch structure with
multi-stage interactions is introduced between correlation information at high
resolution and texture feature at low resolution, to perceive local fine
differences while ensuring global stability. Extensive experiments are
conducted on more comprehensive databases compared to previous works.
Experimental results demonstrate that our method reaches the state-of-the-art
registration performance in terms of accuracy and robustness, while maintaining
considerable competitiveness in efficiency.

提出了一种相位聚合的双分支注册网络（PDRNet），通过在高分辨率的相关信息和低分辨率的纹理特征之间引入双分支结构与多阶段交互，在感知局部细微差异的同时保证全局稳定，从而聚合了传统方法和基于深度学习的方法的优势，实验证明了在准确性和鲁棒性上达到了最先进的注册性能，同时在效率上也具有相当的竞争力。

相位聚合双分支网络用于高效指纹密集匹配

Phase-aggregated Dual-branch Network for Efficient Fingerprint Dense  Registration

Understanding the uncertainty inherent in deep learning-based image
registration models has been an ongoing area of research. Existing methods have
been developed to quantify both transformation and appearance uncertainties
related to the registration process, elucidating areas where the model may
exhibit ambiguity regarding the generated deformation. However, our study
reveals that neither uncertainty effectively estimates the potential errors
when the registration model is used for label propagation. Here, we propose a
novel framework to concurrently estimate both the epistemic and aleatoric
segmentation uncertainties for image registration. To this end, we implement a
compact deep neural network (DNN) designed to transform the appearance
discrepancy in the warping into aleatoric segmentation uncertainty by
minimizing a negative log-likelihood loss function. Furthermore, we present
epistemic segmentation uncertainty within the label propagation process as the
entropy of the propagated labels. By introducing segmentation uncertainty along
with existing methods for estimating registration uncertainty, we offer vital
insights into the potential uncertainties at different stages of image
registration. We validated our proposed framework using publicly available
datasets, and the results prove that the segmentation uncertainties estimated
with the proposed method correlate well with errors in label propagation, all
while achieving superior registration performance.

我们提出了一个新的框架，同时估计图像配准的先验和其它分割不确定性，通过最小化负对数似然损失函数，将外观不一致性转化为其它分割不确定性，并通过引入分割不确定性以及现有的估计配准不确定性方法，在图像配准的不同阶段提供了重要的可视化结果。通过使用公开可用的数据集验证了我们提出的方法，并且结果证明了所提方法估计的分割不确定性与标签传播错误之间的良好关联性，同时实现了优越的配准性能。

从注册不确定性到分割不确定性

From Registration Uncertainty to Segmentation Uncertainty

U-Net style networks are commonly utilized in unsupervised image registration
to predict dense displacement fields, which for high-resolution volumetric
image data is a resource-intensive and time-consuming task. To tackle this
challenge, we first propose Fourier-Net, which replaces the costly U-Net style
expansive path with a parameter-free model-driven decoder. Instead of directly
predicting a full-resolution displacement field, our Fourier-Net learns a
low-dimensional representation of the displacement field in the band-limited
Fourier domain which our model-driven decoder converts to a full-resolution
displacement field in the spatial domain. Expanding upon Fourier-Net, we then
introduce Fourier-Net+, which additionally takes the band-limited spatial
representation of the images as input and further reduces the number of
convolutional layers in the U-Net style network's contracting path. Finally, to
enhance the registration performance, we propose a cascaded version of
Fourier-Net+. We evaluate our proposed methods on three datasets, on which our
proposed Fourier-Net and its variants achieve comparable results with current
state-of-the art methods, while exhibiting faster inference speeds, lower
memory footprint, and fewer multiply-add operations. With such small
computational cost, our Fourier-Net+ enables the efficient training of
large-scale 3D registration on low-VRAM GPUs. Our code is publicly available at
https://github.com/xi-jia/Fourier-Net.

利用傅立叶网络替代资源密集型的 U-Net 风格扩张路径，我们提出了 Fourier-Net，通过学习位于有限带宽傅立叶域中的位移场的低维表示，然后通过模型驱动的解码器将其转换为空间域中的全分辨率位移场，达到减少计算成本、提高注册性能的效果。