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burstinterpolant

Create high-resolution image from set of low-resolution burst mode images

Description

example

imHighRes = burstinterpolant(imds,tforms,scale) creates a high-resolution image, imHighRes from a set of low-resolution burst mode images stored as an ImageDatastore object, imds. The size of imHighRes is scale times the size of the input images. tforms specifies the geometric transformations required to align the low-resolution images.

example

imHighRes = burstinterpolant(images,tforms,scale) creates a high-resolution image, imHighRes from a set of low-resolution burst mode images stored in cell array images.

Examples

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Specify the location of low-resolution burst mode images. The input images are 2-D RGB images with slight variations of rotation and translation.

setDir = fullfile(toolboxdir("images"),"imdata","notebook");

Use the imageDatastore function to read and store the low-resolution burst mode images.

imds = imageDatastore(setDir,"FileExtensions",".png");

Display the images as a montage.

montage(imds)
title("Set of Low-Resolution Burst Mode Images")

Calculate Geometric Transformations

Convert all the RGB images into lightness images by using the rgb2lightness function. The burst mode lightness images are stored as an image datastore object.

imdsTransformed = transform(imds,@(x) rgb2lightness(x));

Get the optimal configuration parameters required for registration of the burst mode lightness images by using the imregconfig function. Specify the image capture modality as "monomodal".

[optimizer,metric] = imregconfig("monomodal");

Find the total number of images stored in the image datastore object by using the numpartitions function.

numImages = numpartitions(imds);

Create an array of 2-D affine transformation objects to store the rigid transformations of each low-resolution burst mode lightness image excluding the reference image. Set the number of rows in the transformation array as total number of images in the image datastore object minus one.

tforms = repmat(affinetform2d,numImages-1,1);

Read the first lightness image into the workspace and use it as the reference image for estimating the geometric transformations.

imgRef = read(imdsTransformed);

Use the imregtform function to estimate a rigid geometric transformation for each low-resolution burst mode lightness image with respect to the reference image.

idx = 1;
while hasdata(imdsTransformed)
    imgMoving = read(imdsTransformed);
    tforms(idx) = imregtform(imgRef,imgMoving,"rigid",optimizer,metric);
    idx = idx+1;
end

Create High-Resolution Image

Specify a scale factor for generating the high-resolution image.

scale = 4;

Create the high-resolution image from the set of low-resolution burst mode RGB images.

imgHighRes = burstinterpolant(imds,tforms,scale);

Display the high-resolution image.

figure("WindowState","maximized")
imshow(imgHighRes)
title("High-Resolution Image")

Display the size of a low-resolution image.

imgLowRes = read(imds);
inputDim = size(imgLowRes)
inputDim = 1×3

   161   186     3

Display the size of the high-resolution image. Because the scale factor is 4, the spatial dimensions of the high-resolution image are 4 times the size of the low-resolution burst mode RGB images.

outputDim = size(imgHighRes)
outputDim = 1×3

   644   744     3

Load a cell array containing the low-resolution burst mode images. The input images are 2-D RGB images with slight variations of rotation and translation.

load("LRData")

Display the images as a montage.

montage(images,"Size",[2 4],"BackgroundColor",[1 1 1])
title("Set of Low-Resolution Burst Mode Images")

Calculate Geometric Transformation Parameters

Convert all the RGB images into lightness images by using the rgb2lightness function.

imagesT = cellfun(@rgb2lightness,images,"UniformOutput",false);

Get the optimal configuration parameters required for registration of the burst mode lightness images by using the imregconfig function. Specify the image capture modality as "monomodal".

[optimizer,metric] = imregconfig("monomodal");

Find the total number of images stored in the cell array.

numImages = length(images);

Create an array of 2-D affine transformation objects to store the rigid transformations of each low-resolution burst mode lightness image excluding the reference image. Set the number of rows in the transformation array as total number of images in the image datastore object minus one.

tforms = repmat(affinetform2d,numImages-1,1);

Read the first lightness image into the workspace and use it as the reference image for estimating the geometric transformations.

imgRef = imagesT{1};

Use the imregtform function to estimate a rigid geometric transformation for each low-resolution burst mode lightness image with respect to the reference image.

for i= 2:length(images)
    imgMoving = imagesT{i};
    tforms(i-1) = imregtform(imgRef,imgMoving,"rigid",optimizer,metric);
end

Create High-Resolution Image

Specify the scale factor for generating the high-resolution image.

scale = 3;

Construct the high-resolution image from the set of low-resolution burst mode RGB images.

imgHighRes = burstinterpolant(images,tforms,scale);

Display the high-resolution image.

imshow(imgHighRes)
title("High-Resolution Image")

Display the size of a low-resolution image.

imgLowRes = images{1};
inputDim = size(imgLowRes)
inputDim = 1×3

   154   265     3

Display the size of the high-resolution image. Because the scale factor is 3, the spatial dimensions of the high-resolution image are 3 times the size of the low-resolution image.

ouputDim = size(imgHighRes)
ouputDim = 1×3

   462   795     3

Input Arguments

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Input image datastore, specified as an ImageDatastore object. The input image datastore contains multiple low-resolution burst mode images used for creating the high-resolution image output.

  • Images in the input image datastore must be 2-D grayscale images of size m-by-n or 2-D RGB images of size m-by-n-by-3.

  • All images in the input image datastore must be of the same size and data type.

  • The number of images in the input image datastore must be greater than or equal to 2.

  • The images must be of data type single, double, uint8, or uint16.

Input images, specified as a k-by-1 cell array. k is the number of input images stored in the cell array. All of the input images must have same size.

Data Types: single | double | uint8 | uint16

Geometric transformation, specified as a vector of geometric transformation object listed in the table. The vector has length k-1, where k is the number of images in imds or images. The n-th element of tforms indicates the geometric transformation used to register the (n+1)-th low-resolution image in imds or images to the first image.

Geometric Transformation ObjectDescription
transltform2dTranslation transformation
rigidtform3dRigid transformation: translation and rotation
simtform2dSimilarity transformation: translation, rotation, and isotropic scaling
affinetform2dAffine transformation: translation, rotation, anisotropic scaling, reflection, and shearing

You can calculate the geometric transformation for each input image using the imregtform function. Specify the reference image as the first image in imds or images.

Note

You can also specify tform as a vector of affine2d objects. However, these objects are not recommended. For more information, see Compatibility Considerations.

Resize factor, specified as a numeric scalar greater than or equal to 1.

Output Arguments

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High-resolution image, returned as a 2-D grayscale image or 2-D RGB image. imHighRes is of the same data type as the input images. The size of imHighRes is the value of scale times the size of the images in imds or images.

For example, let L be the value of scale, and m-by-n be the size of the low-resolution burst mode images. Then, the size of the high-resolution image is mL-by-nL.

Algorithms

The burstinterpolant function uses the inverse distance weighting method to generate a high-resolution image from a set of low-resolution burst mode images [1]. The use of geometric transformations tforms makes the pixel selection robust to rigid geometric transformations (rotations and translations only).

Note

  • If the input images are 2-D RGB images, estimate tforms from the lightness component. You can use the rgb2lightness function to compute lightness values from the RGB color values.

References

[1] Shepard, Donald. “A Two-Dimensional Interpolation Function for Irregularly-Spaced Data”, In Proceedings of the 1968 23rd ACM National Conference, 517-524. New York, NY: ACM, 1968.

Version History

Introduced in R2019a

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