Perform Edge-Preserving Image Smoothing Using OpenCV in MATLAB

This example shows how to perform edge-preserving image smoothing by using prebuilt MATLAB® interface to the OpenCV function cv::edgePreservingFilter. In this example, you also use the createMat utility function to define the input and output arrays, and the getImage utility function to read the output image returned by the OpenCV function.

Add the MATLAB interface to OpenCV package names to the import list.

import clib.opencv.*;
import vision.opencv.util.*;

Read an image into the MATLAB workspace.

img = imread("peppers.png");

Create MATLAB interface objects for the OpenCV MatND and InputArray classes to store the input image.

[inputMat,inputArray] = createMat(img);

Create MATLAB interface objects for the OpenCV MatND and OutputArray classes to write the output image returned by the OpenCV function.

[outputMat,outputArray] = createMat;

Smooth the input image by using the OpenCV function cv::edgePreservingFilter. To call this function from MATLAB, you must use the notation cv.edgePreservingFilter.

cv.edgePreservingFilter(inputArray,outputArray);

Read the filtered output image returned by the OpenCV function.

filteredImg = getImage(outputArray);

Display the original input and the filtered output images.

figure
imshow(img)
title("Input Image")

figure
imshow(filteredImg)
title("Filtered Output Image")

Modify Filter Parameters

Specify the parameter values for the edge-preserving filter. Set these values:

• flags to 2, to perform normalized convolution filtering.

• Standard deviations sigma_s to 20 and sigma_r to 0.3.

flags = 2;
sigma_s = 20;
sigma_r = 0.2;

Perform filtering by using the defined filter parameters.

cv.edgePreservingFilter(inputArray,outputArray,flags,sigma_s,sigma_r);

Read the filtered output image returned by the OpenCV function.

filteredImg = getImage(outputArray);

Display the filtered output image.

figure
imshow(filteredImg)
title("Filtered Output Image")