Histogram matching in digital image processing

In this video, we talk about Histogram matching in digital image processing which includes equalization and mapping of histograms.

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Check out our previous videos!

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Logarithmic Transformation and power-law Transformation in digital image processing with examples -

Image Enhancement in digital image processing with Histogram Equalization -

[Dheemantha]

What happend to remaining 10 pixels, and also there is a chance that final histogram have more pixel than the image.

[gauravkumar]

This is probably my last time on this channel.
Thank you for all the help
(Final year Btech Student)

[arslanytcrazxx]

SAVE MY DAY BEFORE PAPER . THIS VIDEO HELPS ALOT.

[arpitgoyal]

Last minute semester savior amazing and really helpful content. 👌

[suhasshelar]

Hey ur a life saver!! Hats off great explanation

[subashadhikari]

Thankyou so much from the land of mountains, Nepal

[gudavenkatasaikousik]

I think, the reason for the loss of 10 pixels is because of taking the nearest lesser gray level into consideration...

[akeemlouigarde]

@5:45 why does Histogram 2 need to be greater than Histogram 1?

[Mr.X-K]

Thank you so much . Beautiful represention.

[shreekarsiri]

short and sweet explanation.... thankyou

[parameshkumar]

ig this is wrong because the no of pixels values of the original image and final image are not same.

[faaizuddin]

Thank you for uploading this kind of video its help me for preparations of exam.🙂
🇵🇰🇵🇰🇵🇰

[muralisubbarao]

A simple unified way to treat both histogram equalization and histogram specification is the following: represent each pixel as a triple: (row index, column index, gray-level value). Now, sort all the pixels in the input image based on their grey-level in increasing order and store all the pixels of the input image in the triple format-- (row, column, value) - in an array whose size is equal to the number of pixels in the input image. This can be done efficiently as the number of gray-levels is limited, with number of operations proportional to the number of pixels (not N logN, but only N). If the entries of the given histogram to be matched are h0, h1, h2, ..., hk, with k+1 grey levels, then the output image having this histogram is computed easily as follows. As the pixels are sorted in increasing order, the monotonic transformation of gray-values is naturally satisfied. The first h0 pixels in the sorted list of pixels is assigned value 0, the next h1 pixels in the sorted list of pixels is assigned value 1, then the next h2 pixels are assigned value 2, and so on until the last hk pixels are assigned the value k. This assignment is done easily because the sorted list contains not only the gray level value of each pixel, but it also contains the associated position of the pixel in the image specified by row index and the column index, because the pixels are stored as triplets (row, column, value) in the sorted list. One efficient algorithm for sorting the pixels in time proportional to the number of pixels is this: use an array of k linked lists, one list corresponding to each grey level, and inserting each pixel into the list corresponding to its gray value, by scanning the input image for each pixel, and inserting all the pixels. After this, merge the k lists by simply appending the second list to the end of the first, the third list to the end of the second, the fourth list to the end of the third, etc. This merging is similar to that found in the radix-sort algorithm, but with only one iteration. There is an easier way to sort pixels using the cumulative histogram and an array instead of linked lists. That is left as an exercise for the reader! There are variations/modifications of this algorithm that avoid the sorted list but use only the cumulative histograms, and those that map pixels with same values in the input image to a new but same values in the output image, etc. But those are details that can be studied by a more serious student of digital image processing. Good luck! If you have questions, post your email address here in reply. I will answer the first 10 questions. I have the source code in C++.

[yannickraam]

After the mapping do you still have to normalize? Because the new histogram has 380 pixels, which is 10 less than the original

[emreipek]

Many many thanks for the video. U helped me to understand this subject :)

[understandayurveda]

Mam, I have a doubt.
The last 2 columns for histogram 1 are Histogram equalization level and Nk. that means Nk is the last column.
But for histogram 2 the last column is Histogram equalization and not Nk.
Why you did not calculated Nk for histogram 2.

[grayyan]

Mapping should be done from the input image to the specified image.
The video did it reversely.

[shivamgoyal]

I have a question, the total number of pixels in the final histogram is not matching with the first.

[ShwetaGajbhiye-vz]

Waiting for new playlist on DIP dear Anushree....Ma'am.

[kirthika]

Acutally there is some formula to perform histogram matching.. if i use this method, can i get full marks ?