Image Registration

Align two images using intensity correlation, feature matching, or control point mapping

Image registration is the process of aligning two or more images. This process involves designating one image as the reference image, also called the fixed image, and applying geometric transformations or local displacements to the other image so that it aligns with the reference image. Images can be misaligned for a variety of reasons. Commonly, images are captured under variable conditions that can change the camera perspective or the content of the scene.

Together, Image Processing Toolbox™ and Computer Vision Toolbox™ offer four image registration solutions: interactive registration with the Registration Estimator app, intensity-based automatic image registration, control point registration, and automated feature matching. For help selecting which approach to use, see Choose an Image Registration Technique.

Apps

Registration Estimator

Functions

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Automatic Registration

`imregister`	Intensity-based image registration
`imregconfig`	Configurations for intensity-based registration
`imregtform`	Estimate geometric transformation that aligns two 2-D or 3-D images
`imregcorr`	Estimate geometric transformation that aligns two 2-D images using cross correlation
`imregdemons`	Estimate displacement field that aligns two 2-D or 3-D images
`imregmtb`	Register 2-D images using median threshold bitmaps
`normxcorr2`	Normalized 2-D cross-correlation
`MattesMutualInformation`	Mattes mutual information metric configuration
`MeanSquares`	Mean square error metric configuration
`RegularStepGradientDescent`	Regular step gradient descent optimizer configuration
`OnePlusOneEvolutionary`	One-plus-one evolutionary optimizer configuration

Control Point Registration

`cpselect`	Control Point Selection tool
`fitgeotform2d`	Fit 2-D geometric transformation to control point pairs (Since R2022b)
`cpcorr`	Tune control point locations using cross-correlation
`cpstruct2pairs`	Extract valid control point pairs from `cpstruct` structure
`imwarp`	Apply geometric transformation to image

Spatial Referencing

`imref2d`	Reference 2-D image to world coordinates
`imref3d`	Reference 3-D image to world coordinates

Topics

Register Images Interactively

Register Images Using Registration Estimator App

Align a pair of images using feature-based registration techniques in the Registration Estimator app.

Register Images Using Intensity-Based Optimization

Intensity-Based Automatic Image Registration
Intensity-based automatic image registration uses a similarity metric, an optimizer, and a transformation type to register two images iteratively.
Create an Optimizer and Metric for Intensity-Based Image Registration
Select an image metric and an optimizer suitable for either monomodal or multimodal images.
Use Phase Correlation as Preprocessing Step in Registration
Use phase correlation to estimate an initial transformation when images are severely misaligned.
Register an Image Using Normalized Cross-Correlation
Determine the translation needed to align a cropped subset of an image with the larger image.

Register Images Using Control Point Mapping

Control Point Registration
To determine the parameters of a geometric transformation, you can pick corresponding pairs of points in two images.
Select Matching Control Point Pairs
Use the Control Point Selection tool to pick control points in the image to be registered and corresponding points in the fixed image.

Featured Examples

Find Image Rotation and Scale

Use control points to align two images that differ by a rotation and scale change.

Open Live Script

Register Multimodal MRI Images

Align two multimodal MRI images to a common coordinate system using automatic intensity-based image registration.

Open Live Script

Register Multimodal 3-D Medical Images

Align two volumetric images using automatic intensity-based image registration.

Open Live Script

Register Images with Projection Distortion Using Control Points

Open Live Script

Automatically Find Image Rotation and Scale

Demonstrates how to automatically determine the geometric transformation between two images. Specifically, when one image is distorted in relation to another due to rotation and scaling, the functions detectSURFFeatures and estimateGeometricTransform2D can be employed to identify the rotation angle and scale factor. Subsequently, these parameters can be used to transform the distorted image back to its original appearance.

(Computer Vision Toolbox)

Create Panorama

Automatically stitch multiple images into panorama. The procedure for image stitching is an extension of feature based image registration. Instead of registering a single pair of images, multiple image pairs are successively registered relative to each other to form a panorama.

(Computer Vision Toolbox)

Stabilize Video Using Image Point Features

Stabilize a video that was captured from a jittery platform. One way to stabilize a video is to track a salient feature in the image and use this as an anchor point to cancel out all perturbations relative to it. This procedure, however, must be bootstrapped with knowledge of where such a salient feature lies in the first video frame. In this example, we explore a method of video stabilization that works without any such a priori knowledge. It instead automatically searches for the "background plane" in a video sequence, and uses the observed distortion to correct for camera motion.

(Computer Vision Toolbox)