inpaintExemplar

Restore specific image regions using exemplar-based image inpainting

Syntax

J = inpaintExemplar(I,mask)

J = inpaintExemplar(I,mask,Name,Value)

Description

J = inpaintExemplar(I,mask) fills specific regions in the input image using the exemplar-based inpainting method. mask is a logical image that denotes the target regions in the image to be filled using inpainting.

J = inpaintExemplar(I,mask,Name,Value) specifies additional inpainting options using one or more name-value arguments.

Examples

Remove Objects in Image Using Inpainting

Read an image into the workspace.

I = imread('liftingbody.png');

Display the image.

figure
imshow(I,[])

Use the drawellipse function to select an elliptical region of interest (ROI) for inpainting. Use the 'Center' and 'SemiAxes' name-value pairs to specify the location of an ROI.

h = drawellipse('Center',[410 155],'SemiAxes',[95 20]);

Use the createMask function to generate a mask from the selected ROIs.

mask = createMask(h);

Display the image to be inpainted and its corresponding mask image.

montage({I,mask});
title(['Image to Be Inpainted','   |   ','Mask for Inpainting'])

Remove objects in the ROI by using inpainting.

J = inpaintExemplar(I,mask);

Display the original image and the inpainted image.

montage({I,J});
title(['Image to Be Inpainted','    |    ','Inpainted Image']);

Restore Distorted Image Regions Using Inpainting

Read an image into the workspace.

I = imread('forestdistorted.png');

Display the image. The image comprises distorted regions to be restored using inpainting.

figure
imshow(I,[])

Read a binary mask image containing the distorted image regions into the workspace.

mask = imread('imagemask.png');

Display the image to be inpainted and its corresponding mask image.

montage({I,mask});
title(['Image to Be Inpainted','   |   ','Mask for Inpainting'])

Inpaint the original image to restore the distorted image region. Specify the fill order and the patch size for inpainting as tensor and 7, respectively.

J = inpaintExemplar(I,mask,'FillOrder','tensor','PatchSize',7);

Display the original image and the inpainted image.

montage({I,J});
title(['Image to Be Inpainted','   |   ','Inpainted Image'])

Input Arguments

I — Image to be inpainted

2-D grayscale image | RGB image

Image to be inpainted, specified as a 2-D grayscale image or an RGB image of size m-by-n.

Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32

mask — Spatial mask of target regions

2-D binary image

Spatial mask of target regions, specified as a 2-D binary image of the same size as the input image I. The nonzero pixels in mask specify the target regions to be filled using inpainting.

Data Types: logical

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: J = inpaintExemplar(I,mask,"FillOrder","gradient")

FillOrder — Filling order

"gradient" (default) | "tensor"

Filling order, specified as "gradient" or "tensor". The filling order denotes the priority function to be used for calculating the patch priority. The patch priority value specifies the order of filling for the image patches in target regions.

Data Types: char | string

PatchSize — Size of image patch

[9 9] (default) | scalar | vector

Size of the image patch, specified as one of these values.

• A scalar, s — The image patch is a square region of size s-by-s.

• A vector of form [p s] — The image patch is a square or rectangular region of size p-by-s.

The default image patch size is 9-by-9. An image patch refers to the image region considered for patch matching and inpainting.

Note

The size of the image patch must be at least 3-by-3 and always less than the size of the input image.

Data Types: double

Output Arguments

J — Inpainted image

2-D grayscale image | RGB image

Inpainted image, returned as a 2-D grayscale image or an RGB image of the same size and data type as input image I.

Algorithms

The exemplar-based image inpainting algorithm is a patch-based approach that restores target regions in the input image by using these steps.

1. Identify target regions from the input image.

2. Generate a binary mask of the same size as the input image. The nonzero pixels in the mask image must correspond to the target regions to be inpainted.

3. Identify the source region. All regions, excluding the target regions, in the input image comprise the source region. That is, source region = input image − target regions.

4. For every patch of size p-by-s centered on a boundary pixel in the target region, compute the patch priority by using the gradient or tensor method.

5. Find the patch with the maximum priority. This patch constitutes the target patch to be inpainted.

6. Given the target patch, search for the best-matching patch in the source region by using the sum of square difference (SSD).

7. Copy image data from the best-matching patch to the target patch.

8. Update the input image, mask, and patch priority value.

9. Repeat steps 4–8 until the target regions are inpainted.

References

[1] Criminisi, A., P. Perez, and K. Toyama. "Region Filling and Object Removal by Exemplar-Based Image Inpainting." IEEE Transactions on Image Processing. Vol. 13, No. 9, 2004, pp. 1200–1212.

[2] Le Meur, O., M. Ebdelli, and C. Guillemot. "Hierarchical Super-Resolution-Based-Inpainting." IEEE Transactions on Image Processing. Vol. 22, No. 10, 2013, pp. 3779–3790.

Extended Capabilities

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

Version History

Introduced in R2019b

R2022b: Support for thread-based environments

inpaintExemplar now supports thread-based environments.