diff --git a/modules/xphoto/tutorials/fsr_for_inpainting.markdown b/modules/xphoto/tutorials/fsr_for_inpainting.markdown
new file mode 100644
index 000000000..739dda703
--- /dev/null
+++ b/modules/xphoto/tutorials/fsr_for_inpainting.markdown
@@ -0,0 +1,77 @@
+Image Inpainting {#tutorial_xphoto_inpainting}
+================
+
+Introduction
+------------
+In this tutorial we will show how to use the algorithm Rapid Frequency Selective Reconstructiom (FSR) for image inpainting.
+
+Basics
+------
+Image Inpainting is the process of reconstructing damaged or missing parts of an image.
+This is achieved by replacing distorted pixels by pixels similar to the neighboring ones. There are several algorithms for inpainting, using different approaches for such replacement.
+
+One of those algorithms is called **Rapid Frequency Selectice Reconstruction (FSR)**.
+FSR reconstructs image signals by exploiting the property that small areas of images can be represented sparsely in the Fourier domain. See @cite GenserPCS2018 and @cite SeilerTIP2015 for details.
+
+FSR can be utilized for the following areas of application:
+
+-# **Error Concealment (Inpainting)**:
+    The sampling mask indicates the missing pixels of the distorted input image to be reconstructed.
+
+-# **Non-Regular Sampling**:
+    For more information on how to choose a good sampling mask, please review @cite GroscheICIP2018 and @cite GroscheIST2018.
+
+Example
+-------
+The following sample code shows how to use FSR for inpainting.
+The non-zero pixels of the error mask indicate valid image area, while zero pixels indicate area to be reconstructed.
+You can create an arbitrary mask manually using tools like Paint or GIMP. Start with a plain white image and draw some distortions in black.
+
+  @code{.cpp}
+
+  #include <opencv2/opencv.hpp>
+  #include <opencv2/xphoto/inpainting.hpp>
+  #include <iostream>
+
+  using namespace cv;
+
+  int main(int argc, char** argv)
+  {
+      // read image and error pattern
+      Mat original_, mask_;
+      original_ = imread("images/kodim22.png");
+      mask_ = imread("images/pattern_random.png", IMREAD_GRAYSCALE);
+
+      // make sure that mask and source image have the same size
+      Mat mask;
+      resize(mask_, mask, original_.size(), 0.0, 0.0, cv::INTER_NEAREST);
+
+      // distort image
+      Mat im_distorted(original_.size(), original_.type(), Scalar::all(0));
+      original_.copyTo(im_distorted, mask); // copy valid pixels only (i.e. non-zero pixels in mask)
+
+      // reconstruct the distorted image
+      // choose quality profile fast (xphoto::INPAINT_FSR_FAST) or best (xphoto::INPAINT_FSR_BEST)
+      Mat reconstructed;
+      xphoto::inpaint(im_distorted, mask, reconstructed, xphoto::INPAINT_FSR_FAST);
+
+      imshow("orignal image", original_);
+      imshow("distorted image", im_distorted);
+      imshow("reconstructed image", reconstructed);
+      waitKey();
+
+      return 0;
+  }
+  @endcode
+
+Original and distorted image:
+![image](images/originalVSdistorted.jpg)
+
+Reconstruction:
+![image](images/reconstructed_fastVSbest.jpg)
+
+Left image: fast quality profile (run time 8 seconds). Right image: best quality profile (1 minute 51 seconds).
+
+Additional Resources
+--------------------
+[Comparison of FSR to existing inpainting methods in OpenCV](https://github.com/opencv/opencv_contrib/files/3730212/inpainting_comparison.pdf)
diff --git a/modules/xphoto/tutorials/images/originalVSdistorted.jpg b/modules/xphoto/tutorials/images/originalVSdistorted.jpg
new file mode 100644
index 000000000..2320694ab
Binary files /dev/null and b/modules/xphoto/tutorials/images/originalVSdistorted.jpg differ
diff --git a/modules/xphoto/tutorials/images/reconstructed_fastVSbest.jpg b/modules/xphoto/tutorials/images/reconstructed_fastVSbest.jpg
new file mode 100644
index 000000000..c16ea0353
Binary files /dev/null and b/modules/xphoto/tutorials/images/reconstructed_fastVSbest.jpg differ