Image Registration
235 papers with code • 5 benchmarks • 12 datasets
Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.
Source: Image registration | Wikipedia
( Image credit: Kornia )
Benchmarks
These leaderboards are used to track progress in Image Registration
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Libraries
Use these libraries to find Image Registration models and implementationsDatasets
Medical Segmentation Decathlon
LLVIP
Learn2Reg
SegTHOR
UDIS-D
FIRE
DIR-LAB COPDgene
ALTO
Fetoscopy Placenta Data
FetReg: Largescale Multi-centre Fetoscopy Placenta Dataset
Most implemented papers
Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks
multimodallearning/pdd_net
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Nonlinear image registration continues to be a fundamentally important tool in medical image analysis.
Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging
Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient.
Dual-Stream Pyramid Registration Network
kangmiao15/dual-stream-prnet-plus
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We propose a Dual-Stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D medical image registration.
Learning Deformable Registration of Medical Images with Anatomical Constraints
lucasmansilla/ACRN_Chest_X-ray_IA
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Deformable image registration is a fundamental problem in the field of medical image analysis.
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond
We design a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation in order to integrate advantages and avoid limitations of these two categories of approaches.
Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
The crucial step for localization is to match the current observation to the map.
Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration
We find a performance degradation of these models when brain image pairs with different intensity distribution are presented even with similar structures.
Deep learning based registration using spatial gradients and noisy segmentation labels
TheoEst/abdominal_registration
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Image registration is one of the most challenging problems in medical image analysis.
Unsupervised dynamic modeling of medical image transformation
For this, we use a conditional variational auto-encoder (CVAE) to nonlinearly map the higher-dimensional image to a lower-dimensional space, wherein we model the dynamics with a linear Gaussian state-space model (LG-SSM).
