Merge remote-tracking branch 'upstream/3.4' into merge-3.4

cmake/OpenCVFindLibsPerf.cmake

@@ -35,17 +35,6 @@ if(WITH_IPP)
   endif()
 endif()
 
-# --- IPP Async ---
-
-if(WITH_IPP_A)
-  include("${OpenCV_SOURCE_DIR}/cmake/OpenCVFindIPPAsync.cmake")
-  if(IPP_A_INCLUDE_DIR AND IPP_A_LIBRARIES)
-    ocv_include_directories(${IPP_A_INCLUDE_DIR})
-    link_directories(${IPP_A_LIBRARIES})
-    set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} ${IPP_A_LIBRARIES})
-   endif()
-endif(WITH_IPP_A)
-
 # --- CUDA ---
 if(WITH_CUDA)
   include("${OpenCV_SOURCE_DIR}/cmake/OpenCVDetectCUDA.cmake")

cmake/templates/cvconfig.h.in

@@ -103,9 +103,6 @@
 #cmakedefine HAVE_IPP_ICV
 #cmakedefine HAVE_IPP_IW
 
-/* Intel IPP Async */
-#cmakedefine HAVE_IPP_A
-
 /* JPEG-2000 codec */
 #cmakedefine HAVE_JASPER
 

doc/Doxyfile.in

@@ -227,7 +227,6 @@ SEARCH_INCLUDES        = YES
 INCLUDE_PATH           =
 INCLUDE_FILE_PATTERNS  =
 PREDEFINED             = __cplusplus=1 \
-                         HAVE_IPP_A=1 \
                          CVAPI(x)=x \
                          CV_DOXYGEN= \
                          CV_EXPORTS= \

doc/tutorials/core/how_to_use_OpenCV_parallel_for_/how_to_use_OpenCV_parallel_for_.markdown

@@ -1,7 +1,7 @@
 How to use the OpenCV parallel_for_ to parallelize your code {#tutorial_how_to_use_OpenCV_parallel_for_}
 ==================================================================
 
-@prev_tutorial{tutorial_how_to_use_ippa_conversion}
+@prev_tutorial{tutorial_interoperability_with_OpenCV_1}
 
 Goal
 ----

doc/tutorials/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.markdown

@@ -2,7 +2,7 @@ Interoperability with OpenCV 1 {#tutorial_interoperability_with_OpenCV_1}
 ==============================
 
 @prev_tutorial{tutorial_file_input_output_with_xml_yml}
-@next_tutorial{tutorial_how_to_use_ippa_conversion}
+@next_tutorial{tutorial_how_to_use_OpenCV_parallel_for_}
 
 Goal
 ----

doc/tutorials/core/table_of_content_core.markdown

@@ -93,15 +93,6 @@ understanding how to manipulate the images on a pixel level.
     Look here to shed light on all this questions.
 
 
--   @subpage tutorial_how_to_use_ippa_conversion
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Elena Gvozdeva
-
-    You will see how to use the IPP Async with OpenCV.
-
-
 -   @subpage tutorial_how_to_use_OpenCV_parallel_for_
 
     *Compatibility:* \>= OpenCV 2.4.3

doc/tutorials/imgproc/motion_deblur_filter/motion_deblur_filter.markdown

@@ -0,0 +1,72 @@
+Motion Deblur Filter {#tutorial_motion_deblur_filter}
+==========================
+
+Goal
+----
+
+In this tutorial you will learn:
+
+-   what the PSF of a motion blur image is
+-   how to restore a motion blur image
+
+Theory
+------
+
+For the degradation image model theory and the Wiener filter theory you can refer to the tutorial @ref tutorial_out_of_focus_deblur_filter "Out-of-focus Deblur Filter".
+On this page only a linear motion blur distortion is considered. The motion blur image on this page is a real world image. The blur was caused by a moving subject.
+
+### What is the PSF of a motion blur image?
+
+The point spread function (PSF) of a linear motion blur distortion is a line segment. Such a PSF is specified by two parameters: \f$LEN\f$ is the length of the blur and \f$THETA\f$ is the angle of motion.
+
+![Point spread function of a linear motion blur distortion](images/motion_psf.png)
+
+### How to restore a blurred image?
+
+On this page the Wiener filter is used as the restoration filter, for details you can refer to the tutorial @ref tutorial_out_of_focus_deblur_filter "Out-of-focus Deblur Filter".
+In order to synthesize the Wiener filter for a motion blur case, it needs to specify the signal-to-noise ratio (\f$SNR\f$), \f$LEN\f$ and \f$THETA\f$ of the PSF.
+
+Source code
+-----------
+
+You can find source code in the `samples/cpp/tutorial_code/ImgProc/motion_deblur_filter/motion_deblur_filter.cpp` of the OpenCV source code library.
+
+@include cpp/tutorial_code/ImgProc/motion_deblur_filter/motion_deblur_filter.cpp
+
+Explanation
+-----------
+
+A motion blur image recovering algorithm consists of PSF generation, Wiener filter generation and filtering a blurred image in a frequency domain:
+@snippet samples/cpp/tutorial_code/ImgProc/motion_deblur_filter/motion_deblur_filter.cpp main
+
+A function calcPSF() forms a PSF according to input parameters \f$LEN\f$ and \f$THETA\f$ (in degrees):
+@snippet samples/cpp/tutorial_code/ImgProc/motion_deblur_filter/motion_deblur_filter.cpp calcPSF
+
+A function edgetaper() tapers the input image’s edges in order to reduce the ringing effect in a restored image:
+@snippet samples/cpp/tutorial_code/ImgProc/motion_deblur_filter/motion_deblur_filter.cpp edgetaper
+
+The functions calcWnrFilter(), fftshift() and filter2DFreq() realize an image filtration by a specified PSF in the frequency domain.  The functions are copied from the tutorial
+@ref tutorial_out_of_focus_deblur_filter "Out-of-focus Deblur Filter".
+
+Result
+------
+
+Below you can see the real world image with motion blur distortion. The license plate is not readable on both cars. The red markers show the car’s license plate location.
+![Motion blur image. The license plates are not readable](images/motion_original.jpg)
+
+
+Below you can see the restoration result for the black car license plate. The result has been computed with \f$LEN\f$ = 125, \f$THETA\f$ = 0, \f$SNR\f$ = 700.
+![The restored image of the black car license plate](images/black_car.jpg)
+
+Below you can see the restoration result for the white car license plate. The result has been computed with \f$LEN\f$ = 78, \f$THETA\f$ = 15, \f$SNR\f$ = 300.
+![The restored image of the white car license plate](images/white_car.jpg)
+
+The values of \f$SNR\f$, \f$LEN\f$ and \f$THETA\f$ were selected manually to give the best possible visual result. The \f$THETA\f$ parameter coincides with the car’s moving direction, and the
+\f$LEN\f$ parameter depends on the car’s moving speed.
+The result is not perfect, but at least it gives us a hint of the image’s content. With some effort, the car license plate is now readable.
+
+@note The parameters \f$LEN\f$ and \f$THETA\f$ are the most important. You should adjust \f$LEN\f$ and \f$THETA\f$ first, then \f$SNR\f$.
+
+You can also find a quick video demonstration of a license plate recovering method
+[YouTube](https://youtu.be/xSrE0hdhb4o).
+@youtube{xSrE0hdhb4o}