Image Registration
236 papers with code • 5 benchmarks • 11 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 )
Libraries
Use these libraries to find Image Registration models and implementationsDatasets
Most implemented papers
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images.
Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network
3D medical image registration is of great clinical importance.
Recursive Cascaded Networks for Unsupervised Medical Image Registration
We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration.
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems.
Indirect Image Registration with Large Diffeomorphic Deformations
The paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting where a template is registered against a target that is given through indirect noisy observations.
An Unsupervised Learning Model for Deformable Medical Image Registration
We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest.
TractSeg - Fast and accurate white matter tract segmentation
The individual course of white matter fiber tracts is an important key for analysis of white matter characteristics in healthy and diseased brains.
AirLab: Autograd Image Registration Laboratory
With the "Autograd Image Registration Laboratory" (AIRLab), we introduce an open laboratory for image registration tasks, where the analytic gradients of the objective function are computed automatically and the device where the computations are performed, on a CPU or a GPU, is transparent.
Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning
We demonstrate that this idea can be combined with a video-prediction based controller to enable complex behaviors to be learned from scratch using only raw visual inputs, including grasping, repositioning objects, and non-prehensile manipulation.
MRI to CT Translation with GANs
We present a detailed description and reference implementation of preprocessing steps necessary to prepare the public Retrospective Image Registration Evaluation (RIRE) dataset for the task of magnetic resonance imaging (MRI) to X-ray computed tomography (CT) translation.