doc: fix English gramma in tutorial out-of-focus-deblur filter (#12214)

* doc: fix English gramma in tutorial out-of-focus-deblur filter

* Update out_of_focus_deblur_filter.markdown

slightly modified one sentence

doc/tutorials/imgproc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.markdown

@@ -8,54 +8,54 @@ Goal
 
 In this tutorial you will learn:
 
--   what is a degradation image model
--   what is PSF of out-of-focus image
+-   what a degradation image model is
+-   what the PSF of an out-of-focus image is
 -   how to restore a blurred image
--   what is Wiener filter
+-   what is a Wiener filter
 
 Theory
 ------
 
-@note The explanation is based on the books @cite gonzalez and @cite gruzman. Also, you can refer to Matlab's tutorial [Image Deblurring in Matlab] and an article [SmartDeblur].
-@note An out-of-focus image on this page is a real world  image. An out-of-focus was done manually by camera optics.
+@note The explanation is based on the books @cite gonzalez and @cite gruzman. Also, you can refer to Matlab's tutorial [Image Deblurring in Matlab] and the article [SmartDeblur].
+@note The out-of-focus image on this page is a real world  image. The out-of-focus was achieved manually by camera optics.
 
 ### What is a degradation image model?
 
-A mathematical model of the image degradation in frequency domain representation is:
+Here is a mathematical model of the image degradation in frequency domain representation:
 
 \f[S = H\cdot U + N\f]
 
 where
 \f$S\f$ is a spectrum of blurred (degraded) image,
 \f$U\f$ is a spectrum of original true (undegraded) image,
-\f$H\f$ is frequency response of point spread function (PSF),
+\f$H\f$ is a frequency response of point spread function (PSF),
 \f$N\f$ is a spectrum of additive noise.
 
-Circular PSF is a good approximation of out-of-focus distortion. Such PSF is specified by only one parameter - radius \f$R\f$. Circular PSF is used in this work.
+The circular PSF is a good approximation of out-of-focus distortion. Such a PSF is specified by only one parameter - radius \f$R\f$. Circular PSF is used in this work.
 
 ![Circular point spread function](psf.png)
 
-### How to restore an blurred image?
+### How to restore a blurred image?
 
-The objective of restoration (deblurring) is to obtain an estimate of the original image. Restoration formula in frequency domain is:
+The objective of restoration (deblurring) is to obtain an estimate of the original image. The restoration formula in frequency domain is:
 
 \f[U' = H_w\cdot S\f]
 
 where
-\f$U'\f$ is spectrum of estimation of original image \f$U\f$,
-\f$H_w\f$ is restoration filter, for example, Wiener filter.
+\f$U'\f$ is the spectrum of estimation of original image \f$U\f$, and
+\f$H_w\f$ is the restoration filter, for example, the Wiener filter.
 
-### What is Wiener filter?
+### What is the Wiener filter?
 
-Wiener filter is a way to restore a blurred image. Let's suppose that PSF is a real and symmetric signal, a power spectrum of the original true image and noise are not known,
-then simplified Wiener formula is:
+The Wiener filter is a way to restore a blurred image. Let's suppose that the PSF is a real and symmetric signal, a power spectrum of the original true image and noise are not known,
+then a simplified Wiener formula is:
 
 \f[H_w = \frac{H}{|H|^2+\frac{1}{SNR}} \f]
 
 where
 \f$SNR\f$ is signal-to-noise ratio.
 
-So, in order to recover an out-of-focus image by Wiener filter, it needs to know \f$SNR\f$ and \f$R\f$ of circular PSF.
+So, in order to recover an out-of-focus image by Wiener filter, it needs to know the \f$SNR\f$ and \f$R\f$ of the circular PSF.
 
 
 Source code
@@ -68,36 +68,36 @@ You can find source code in the `samples/cpp/tutorial_code/ImgProc/out_of_focus_
 Explanation
 -----------
 
-An out-of-focus image recovering algorithm consists of PSF generation, Wiener filter generation and filtering an blurred image in frequency domain:
+An out-of-focus image recovering algorithm consists of PSF generation, Wiener filter generation and filtering a blurred image in frequency domain:
 @snippet samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp main
 
-A function calcPSF() forms an circular PSF according to input parameter radius \f$R\f$:
+A function calcPSF() forms a circular PSF according to input parameter radius \f$R\f$:
 @snippet samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp calcPSF
 
-A function calcWnrFilter() synthesizes simplified Wiener filter \f$H_w\f$ according to formula described above:
+A function calcWnrFilter() synthesizes the simplified Wiener filter \f$H_w\f$ according to the formula described above:
 @snippet samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp calcWnrFilter
 
-A function fftshift() rearranges PSF. This code was just copied from tutorial @ref tutorial_discrete_fourier_transform "Discrete Fourier Transform":
+A function fftshift() rearranges the PSF. This code was just copied from the tutorial @ref tutorial_discrete_fourier_transform "Discrete Fourier Transform":
 @snippet samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp fftshift
 
-A function filter2DFreq() filters an blurred image in frequency domain:
+A function filter2DFreq() filters the blurred image in the frequency domain:
 @snippet samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp filter2DFreq
 
 Result
 ------
 
-Below you can see real out-of-focus image:
+Below you can see the real out-of-focus image:
 ![Out-of-focus image](images/original.jpg)
 
 
-Below result was done by \f$R\f$ = 53 and \f$SNR\f$ = 5200 parameters:
+And the following result has been computed with \f$R\f$ = 53 and \f$SNR\f$ = 5200 parameters:
 ![The restored (deblurred) image](images/recovered.jpg)
 
-The Wiener filter was used, values of \f$R\f$ and \f$SNR\f$ were selected manually to give the best possible visual result.
-We can see that the result is not perfect, but it gives us a hint to the image content. With some difficulty, the text is readable.
+The Wiener filter was used, and values of \f$R\f$ and \f$SNR\f$ were selected manually to give the best possible visual result.
+We can see that the result is not perfect, but it gives us a hint to the image's content. With some difficulty, the text is readable.
 
 @note The parameter \f$R\f$ is the most important. So you should adjust \f$R\f$ first, then \f$SNR\f$.
-@note Sometimes you can observe the ringing effect in an restored image. This effect can be reduced by several methods. For example, you can taper input image edges.
+@note Sometimes you can observe the ringing effect in a restored image. This effect can be reduced with several methods. For example, you can taper input image edges.
 
 You can also find a quick video demonstration of this on
 [YouTube](https://youtu.be/0bEcE4B0XP4).