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Local entropy of grayscale image


J = entropyfilt(I)
J = entropyfilt(I,NHOOD)


J = entropyfilt(I) returns the array J, where each output pixel contains the entropy value of the 9-by-9 neighborhood around the corresponding pixel in the input image I. I can have any dimension. If I has more than two dimensions, entropyfilt treats it as a multidimensional grayscale image and not as a truecolor (RGB) image. The output image J is the same size as the input image I.

For pixels on the borders of I, entropyfilt uses symmetric padding. In symmetric padding, the values of padding pixels are a mirror reflection of the border pixels in I.

J = entropyfilt(I,NHOOD) performs entropy filtering of the input image I where you specify the neighborhood in NHOOD. NHOOD is a multidimensional array of zeros and ones where the nonzero elements specify the neighbors. NHOOD's size must be odd in each dimension.

By default, entropyfilt uses the neighborhood true(9). entropyfilt determines the center element of the neighborhood by floor((size(NHOOD) + 1)/2). To specify neighborhoods of various shapes, such as a disk, use the strel function to create a structuring element object and then use the getnhood function to extract the neighborhood from the structuring element object.

Class Support

I can be logical, uint8, uint16, or double, and must be real and nonsparse. NHOOD can be logical or numeric and must contain zeros or ones. The output array J is of class double.

entropyfilt converts any class other than logical to uint8 for the histogram count calculation so that the pixel values are discrete and directly correspond to a bin value.


I = imread('circuit.tif'); 
J = entropyfilt(I); 
imshow(I), figure, imshow(J,[]); 


[1] Gonzalez, R.C., R.E. Woods, S.L. Eddins, Digital Image Processing Using MATLAB, New Jersey, Prentice Hall, 2003, Chapter 11.

Introduced before R2006a

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