EntropyFilter

EntropyFilter[data,r]

filters data by replacing every value by the entropy value in its range-r neighborhood.

EntropyFilter[data,{r1,r2,…}]

uses ri for filtering the ^(th)dimension in data.

Details

  • EntropyFilter returns the local randomness of a signal, commonly used to measure textures in an image. The size of the neighborhood is dependent on the value of r.
  • The function applied to each range-r neighborhood is Entropy.
  • The data can be any of the following:
  • listarbitrary-rank numerical array
    tseriestemporal data such as TimeSeries, TemporalData, …
    imagearbitrary Image or Image3D object
    audioan Audio object
  • EntropyFilter[data,{r1,r2,…}] computes the entropy value in blocks centered on each sample.
  • EntropyFilter assumes the index coordinate system for lists and images.
  • At the data boundaries, EntropyFilter uses smaller neighborhoods.

Examples

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Basic Examples  (3)

Apply an entropy filter to a vector of numbers:

Filter a TimeSeries:

Entropy filtering of random disks:

Scope  (11)

Data  (7)

Apply a moving entropy filter to a vector:

Entropy filtering of a 2D array:

Filter a quantity array:

Filter an Audio signal:

Filtering a 2D grayscale image:

Entropy filtering of a 3D image:

Filter a symbolic array:

Parameters  (4)

Specify one radius to be used in all directions:

Increasing the radius will result in smoother images:

Harmonic filtering just in the first direction:

Second direction:

Entropy filtering of a 3D image in the vertical direction only:

Filtering of the horizontal planes only:

Applications  (3)

Apply entropy filtering to show areas of higher information content with higher intensities:

Entropy filtering can reveal JPEG compression artifacts:

This reveals the presence of padding in an image:

Properties & Relations  (2)

Entropy filtering is the same as ArrayFilter with function Entropy:

Entropy filtering is the same as ImageFilter with function Entropy:

Possible Issues  (1)

The discrete entropy measure does not apply to real-valued images, since distinct pixel values are unlikely to occur more than once:

Use ColorQuantize to limit the number of possible pixel values:

Wolfram Research (2008), EntropyFilter, Wolfram Language function, https://reference.wolfram.com/language/ref/EntropyFilter.html (updated 2016).

Text

Wolfram Research (2008), EntropyFilter, Wolfram Language function, https://reference.wolfram.com/language/ref/EntropyFilter.html (updated 2016).

CMS

Wolfram Language. 2008. "EntropyFilter." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2016. https://reference.wolfram.com/language/ref/EntropyFilter.html.

APA

Wolfram Language. (2008). EntropyFilter. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/EntropyFilter.html

BibTeX

@misc{reference.wolfram_2024_entropyfilter, author="Wolfram Research", title="{EntropyFilter}", year="2016", howpublished="\url{https://reference.wolfram.com/language/ref/EntropyFilter.html}", note=[Accessed: 22-December-2024 ]}

BibLaTeX

@online{reference.wolfram_2024_entropyfilter, organization={Wolfram Research}, title={EntropyFilter}, year={2016}, url={https://reference.wolfram.com/language/ref/EntropyFilter.html}, note=[Accessed: 22-December-2024 ]}