How to implement a part of histogram equalization in matlab without using for loops and influencing speed and performance

问题

Suppose that I have these Three variables in matlab Variables
I want to extract diverse values in NewGrayLevels and sum rows of OldHistogram that are in the same rows as one diverse value is.
For example you see in NewGrayLevels that the six first rows are equal to zero. It means that 0 in the NewGrayLevels has taken its value from (0 1 2 3 4 5) of OldGrayLevels. So the corresponding rows in OldHistogram should be summed.
So 0+2+12+38+113+163=328 would be the frequency of the gray level 0 in the equalized histogram and so on.
Those who are familiar with image processing know that it's part of the histogram equalization algorithm.
Note that I don't want to use built-in function "histeq" available in image processing toolbox and I want to implement it myself.
I know how to write the algorithm with for loops. I'm seeking if there is a faster way without using for loops.
The code using for loops:

   for k=0:255
       Condition = NewGrayLevels==k;
       ConditionMultiplied = Condition.*OldHistogram;
       NewHistogram(k+1,1) = sum(ConditionMultiplied);
   end

I'm afraid if this code gets slow for high resolution big images.Because the variables that I have uploaded are for a small image downloaded from the internet but my code may be used for sattellite images.

回答1:

I know you say you don't want to use histeq, but it might be worth your time to look at the MATLAB source file to see how the developers wrote it and copy the parts of their code that you would like to implement. Just do edit('histeq') or edit('histeq.m'), I forget which.

Usually the MATLAB code is vectorized where possible and runs pretty quick. This could save you from having to reinvent the entire wheel, just the parts you want to change.

回答2:

I can't think a way to implement this without a for loop somewhere, but one optimisation you could make would be using indexing instead of multiplication:

for k=0:255
    Condition = NewGrayLevels==k; % These act as logical indices to OldHistogram
    NewHistogram(k+1,1) = sum(OldHistogram(Condition)); % Removes a vector multiplication,   some additions, and an index-to-double conversion
end

Edit:

On rereading your initial post, I think that the way to do this without a for loop is to use accumarray (I find this a difficult function to understand, so read the documentation and search online and on here for examples to do so):

NewHistogram = accumarray(1+NewGrayLevels,OldHistogram);

This should work so long as your maximum value in NewGrayLevels (+1 because you are starting at zero) is equal to the length of OldHistogram.

回答3:

Well I understood that there's no need to write the code that @Hugh Nolan suggested. See the explanation here:

%The green lines are because after writing the code, I understood that  
%there's no need to calculate the equalized histogram in  
%"HistogramEqualization" function and after gaining the equalized image  
%matrix you can pass it to the "ExtractHistogram" function  
% (which there's no loops in it) to acquire the  
%equalized histogram.    
%But I didn't delete those lines of code because I had tried a lot to  
%understand the algorithm and write them.

For more information and studying the code, please see my next question.

来源：https://stackoverflow.com/questions/17300788/how-to-implement-a-part-of-histogram-equalization-in-matlab-without-using-for-lo

标签

performance

matlab

image-processing

time

histogram