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+High Dynamic Range (HDR) {#tutorial_py_hdr}
+========================
+
+Goal
+----
+
+In this chapter, we will
+
+- Learn how to generate and display HDR image from an exposure sequence.
+- Use exposure fusion to merge an exposure sequence.
+
+Theory
+------
+
+High-dynamic-range imaging (HDRI or HDR) is a technique used in imaging and photography to reproduce
+a greater dynamic range of luminosity than is possible with standard digital imaging or photographic
+techniques. While the human eye can adjust to a wide range of light conditions, most imaging devices use 8-bits
+per channel, so we are limited to only 256 levels. When we take photographs of a real
+world scene, bright regions may be overexposed, while the dark ones may be underexposed, so we
+can’t capture all details using a single exposure. HDR imaging works with images that use more
+than 8 bits per channel (usually 32-bit float values), allowing much wider dynamic range.
+
+There are different ways to obtain HDR images, but the most common one is to use photographs of
+the scene taken with different exposure values. To combine these exposures it is useful to know your
+camera’s response function and there are algorithms to estimate it. After the HDR image has been
+merged, it has to be converted back to 8-bit to view it on usual displays. This process is called
+tonemapping. Additional complexities arise when objects of the scene or camera move between shots,
+since images with different exposures should be registered and aligned.
+
+In this tutorial we show 2 algorithms (Debvec, Robertson) to generate and display HDR image from an
+exposure sequence, and demonstrate an alternative approach called exposure fusion (Mertens), that
+produces low dynamic range image and does not need the exposure times data.
+Furthermore, we estimate the camera response function (CRF) which is of great value for many computer
+vision algorithms.
+Each step of HDR pipeline can be implemented using different algorithms and parameters, so take a
+look at the reference manual to see them all.
+
+
+Exposure sequence HDR
+---------------------
+
+In this tutorial we will look on the following scene, where we have 4 exposure
+images, with exposure times of: 15, 2.5, 1/4 and 1/30 seconds. (You can download
+the images from [Wikipedia](https://en.wikipedia.org/wiki/High-dynamic-range_imaging))
+
+![image](images/exposures.jpg)
+
+### 1. Loading exposure images into a list
+
+The first stage is simply loading all images into a list.
+In addition, we will need the exposure times for the regular HDR algorithms.
+Pay attention for the data types, as the images should be 1-channel or 3-channels
+8-bit (np.uint8) and the exposure times need to be float32 and in seconds.
+
+@code{.py}
+import cv2
+import numpy as np
+
+# Loading exposure images into a list
+img_fn = ["img0.jpg", "img1.jpg", "img2.jpg", "img3.jpg"]
+img_list = [cv2.imread(fn) for fn in img_fn]
+exposure_times = np.array([15.0, 2.5, 0.25, 0.0333], dtype=np.float32)
+@endcode
+
+### 2. Merge exposures into HDR image
+
+In this stage we merge the exposure sequence into one HDR image, showing 2 possibilities
+which we have in OpenCV. The first method is Debvec and the second one is Robertson.
+Notice that the HDR image is of type float32, and not uint8, as it contains the
+full dynamic range of all exposure images.
+
+@code{.py}
+# Merge exposures to HDR image
+merge_debvec = cv2.createMergeDebevec()
+hdr_debvec = merge_debvec.process(img_list, times=exposure_times.copy())
+merge_robertson = cv2.createMergeRobertson()
+hdr_robertson = merge_robertson.process(img_list, times=exposure_times.copy())
+@endcode
+
+### 3. Tonemap HDR image
+
+We map the 32-bit float HDR data into the range [0..1].
+Actually, in some cases the values can be larger than 1 or lower the 0, so notice
+we will later have to clip the data in order to avoid overflow.
+
+@code{.py}
+# Tonemap HDR image
+tonemap1 = cv2.createTonemapDurand(gamma=2.2)
+res_debvec = tonemap1.process(hdr_debvec.copy())
+tonemap2 = cv2.createTonemapDurand(gamma=1.3)
+res_robertson = tonemap2.process(hdr_robertson.copy())
+@endcode
+
+### 4. Merge exposures using Mertens fusion
+
+Here we show an alternative algorithm to merge the exposure images, where
+we do not need the exposure times. We also do not need to use any tonemap
+algorithm because the Mertens algorithm already gives us the result in the
+range of [0..1].
+
+@code{.py}
+# Exposure fusion using Mertens
+merge_mertens = cv2.createMergeMertens()
+res_mertens = merge_mertens.process(img_list)
+@endcode
+
+### 5. Convert to 8-bit and save
+
+In order to save or display the results, we need to convert the data into 8-bit
+integers in the range of [0..255].
+
+@code{.py}
+# Convert datatype to 8-bit and save
+res_debvec_8bit = np.clip(res_debvec*255, 0, 255).astype('uint8')
+res_robertson_8bit = np.clip(res_robertson*255, 0, 255).astype('uint8')
+res_mertens_8bit = np.clip(res_mertens*255, 0, 255).astype('uint8')
+
+cv2.imwrite("ldr_debvec.jpg", res_debvec_8bit)
+cv2.imwrite("ldr_robertson.jpg", res_robertson_8bit)
+cv2.imwrite("fusion_mertens.jpg", res_mertens_8bit)
+@endcode
+
+Results
+-------
+
+You can see the different results but consider that each algorithm have additional
+extra parameters that you should fit to get your desired outcome. Best practice is
+to try the different methods and see which one performs best for your scene.
+
+### Debvec:
+
+![image](images/ldr_debvec.jpg)
+
+### Robertson:
+
+![image](images/ldr_robertson.jpg)
+
+### Mertenes Fusion:
+
+![image](images/fusion_mertens.jpg)
+
+
+Estimating Camera Response Function
+-----------------------------------
+
+The camera response function (CRF) gives us the connection between the scene radiance
+to the measured intensity values. The CRF if of great importance in some computer vision
+algorithms, including HDR algorithms. Here we estimate the inverse camera response
+function and use it for the HDR merge.
+
+@code{.py}
+# Estimate camera response function (CRF)
+cal_debvec = cv2.createCalibrateDebevec()
+crf_debvec = cal_debvec.process(img_list, times=exposure_times)
+hdr_debvec = merge_debvec.process(img_list, times=exposure_times.copy(), response=crf_debvec.copy())
+cal_robertson = cv2.createCalibrateRobertson()
+crf_robertson = cal_robertson.process(img_list, times=exposure_times)
+hdr_robertson = merge_robertson.process(img_list, times=exposure_times.copy(), response=crf_robertson.copy())
+@endcode
+
+The camera response function is represented by a 256-length vector for each color channel.
+For this sequence we got the following estimation:
+
+![image](images/crf.jpg)
+
+Additional Resources
+--------------------
+
+1.  Paul E Debevec and Jitendra Malik. Recovering high dynamic range radiance maps from photographs.
+	In ACM SIGGRAPH 2008 classes, page 31. ACM, 2008.
+2.	Mark A Robertson, Sean Borman, and Robert L Stevenson. Dynamic range improvement through multiple exposures.
+	In Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on, volume 3, pages 159–163.
+	IEEE, 1999.	
+3.  Tom Mertens, Jan Kautz, and Frank Van Reeth. Exposure fusion. In Computer Graphics and Applications, 2007.
+	PG'07. 15th Pacific Conference on, pages 382–390. IEEE, 2007.	
+4.  Images from [Wikipedia-HDR](https://en.wikipedia.org/wiki/High-dynamic-range_imaging)
+
+Exercises
+---------
+1. Try all tonemap algorithms: [Drago](http://docs.opencv.org/master/da/d53/classcv_1_1TonemapDrago.html),
+	[Durand](http://docs.opencv.org/master/da/d3d/classcv_1_1TonemapDurand.html),
+	[Mantiuk](http://docs.opencv.org/master/de/d76/classcv_1_1TonemapMantiuk.html) and
+	[Reinhard](http://docs.opencv.org/master/d0/dec/classcv_1_1TonemapReinhard.html).
+2. Try changing the parameters in the HDR calibration and tonemap methods.
\ No newline at end of file
diff --git a/doc/py_tutorials/py_photo/py_table_of_contents_photo.markdown b/doc/py_tutorials/py_photo/py_table_of_contents_photo.markdown
index 9a8b03e900..1fd57b98ac 100644
--- a/doc/py_tutorials/py_photo/py_table_of_contents_photo.markdown
+++ b/doc/py_tutorials/py_photo/py_table_of_contents_photo.markdown
@@ -14,3 +14,7 @@ denoising etc.
     Do you have a old
     degraded photo with many black spots and strokes on it? Take it. Let's try to restore them with a
     technique called image inpainting.
+	
+-   @subpage tutorial_py_hdr
+
+    Learn how to merge exposure sequence and process high dynamic range images.
\ No newline at end of file